Qualitative Comparative Analysis (QCA) and Configurational Thinking in Management Studies

Qualitative comparative institutional analysis of environmental governance: Implications from research on payments for ecosystem services

During the last decade, qualitative comparative analysis (QCA) has become an increasingly popular research approach in the management and business literature. As an approach, QCA consists of both a set of analytical techniques and a conceptual perspective, and the origins of QCA as an analytical technique lie outside the management and business literature. In the 1980s, Charles Ragin, a sociologist and political scientist, developed a systematic, comparative methodology as an alternative to qualitative, case-oriented approaches and to quantitative, variable-oriented approaches. Whereas the analytical technique of QCA was developed outside the management literature, the conceptual perspective underlying QCA has a long history in the management literature, in particular in the form of contingency and configurational theory that have played an important role in management theories since the late 1960s. Until the 2000s, management researchers only sporadically used QCA as an analytical technique. Between 2007 and 2008, a series of seminal articles in leading management journals laid the conceptual, methodological, and empirical foundations for QCA as a promising research approach in business and management. These articles led to a “first” wave of QCA research in management. During the first wave—occurring between approximately 2008 and 2014—researchers successfully published QCA-based studies in leading management journals and triggered important methodological debates, ultimately leading to a revival of the configurational perspective in the management literature. Following the first wave, a “second” wave—between 2014 and 2018—saw a rapid increase in QCA publications across several subfields in management research, the development of methodological applications of QCA, and an expansion of scholarly debates around the nature, opportunities, and future of QCA as a research approach. The second wave of QCA research in business and management concluded with researchers’ taking stock of the plethora of empirical studies using QCA for identifying best practice guidelines and advocating for the rise of a “neo-configurational” perspective, a perspective drawing on set-theoretic logic, causal complexity, and counterfactual analysis. Nowadays, QCA is an established approach in some research areas (e.g., organization theory, strategic management) and is diffusing into several adjacent areas (e.g., entrepreneurship, marketing, and accounting), a situation that promises new opportunities for advancing the analytical technique of QCA as well as configurational thinking and theorizing in the business and management literature. To advance the analytical foundations of QCA, researchers may, for example, advance robustness tests for QCA or focus on issues of endogeneity and omitted variables in QCA. To advance the conceptual foundations of QCA, researchers may, for example, clarify the links between configurational theory and related theoretical perspectives, such as systems theory or complexity theory, or develop theories on the temporal dynamics of configurations and configurational change. Ultimately, after a decade of growing use and interest in QCA and given the unique strengths of this approach for addressing questions relevant to management research, QCA will continue to influence research in business and management.

Qualitative Comparative Analysis (QCA) was launched in the late 1980s by Charles Ragin, as a research approach bridging case-oriented and variable-oriented perspectives. It conceives cases as complex combinations of attributes (i.e. configurations), is designed to process multiple cases, and enables one to identify, through minimization algorithms, the core equifinal combinations of conditions leading to an outcome of interest. It systematizes the analysis in terms of necessity and sufficiency, models social reality in terms of set-theoretic relations, and provides powerful logical tools for complexity reduction. It initially came along with one technique, crisp-set QCA (csQCA), requiring dichotomized coding of data. As it has expanded, the QCA field has been enriched by new techniques such as multi-value QCA (mvQCA) and especially fuzzy-set QCA (fsQCA), both of which enable finer-grained calibration. It has also developed further with diverse extensions and more advanced designs, including mixed- and multimethod designs in which QCA is sequenced with focused case studies or with statistical analyses. QCA’s emphasis on causal complexity makes it very fit to address various types of objects and research questions touching upon political decision making—and indeed QCA has been applied in multiple related social scientific fields. While QCA can be exploited in different ways, it is most frequently used for theory evaluation purposes, with a streamlined protocol including a sequence of core operations and good practices. Several reliable software options are also available to implement the core of the QCA procedure. However, given QCA’s case-based foundation, much researcher input is still required at different stages. As it has further developed, QCA has been subject to fierce criticism, especially from a mainstream statistical perspective. This has stimulated further innovations and refinements, in particular in terms of parameters of fit and robustness tests which also correspond to the growth of QCA applications in larger-n designs. Altogether the field has diversified and broadened, and different users may exploit QCA in various ways, from smaller-n case-oriented uses to larger-n more analytic uses, and following different epistemological positions regarding causal claims. This broader field can therefore be labeled as that of both “Configurational Comparative Methods” (CCMs) and “Set-Theoretic Methods” (STMs).

To survive in a competitive environment, small and medium enterprises (SMEs) have had to adapt to the digital environment in order to adjust to customer needs globally, particularly in the post‐COVID‐19 world. The advantages of cloud computing (e.g., flexibility, scalability, and low entry cost) provide opportunities for SMEs with a restricted budget and limited resources. To understand how SMEs adopt cloud computing in a complex digital environment, this study examines how antecedents combine with each other to explain the high adoption of cloud computing. From the perspectives of holism and set theory, we draw on complexity and configuration theories, present a conceptual model including seven antecedents based on the technology‐organization‐environment framework, and conduct an asymmetric fuzzy‐set qualitative comparative analysis. Through an empirical study with 123 Chinese companies, we identify nine combinations (configurations) of determinant antecedents that lead to the high adoption of cloud computing. The results show that none of the factors are indispensable to explain a high adoption on their own; instead, they are insufficient but necessary parts of the causal combinations that explain a high adoption. This study contributes to the literature on cloud computing adoption by extending current knowledge on how antecedents combine to increase the adoption and identify specific patterns of SMEs for whom these factors are essential and greatly influence their adoption.

Invented by the sociologist Charles Ragin in the 80 to identify the configurations explaining a phenomenon, the Qualitative Comparative Analysis (QCA) method has been used in management sciences since mid of years 2000s. Research in management information systems seems to be an exception, with rare papers that appeared recently. However, this method offers several advantages to explore aspects, like equifinality, causal complexity, sensitivity to outliers and attention to the limited diversity of observed configurations. The aim of this paper is to present the features, benefits and limitations of this method for research in IS and an illustration of fuzzy-set QCA, one of the two main versions of QCA. This variant of the method is applied to the issue of the contribution of Product Lifecycle Management (PLM) to respect the planned development time in the co-development of new products. QCA method identifies necessary and sufficient conditions for a result. In the illustration, we identified five possible configurations for a good respect of development time. In particular, we have highlighted a configuration where the use of the three PLM sub-systems is sufficient for a good respect of development time. QCA method is used to address the causal complexity notably through equifinality and the management of asymmetric configurations for a positive and negative result.

Quantitative studies in marketing are dominated by variance-based approaches. These have limitations for understanding macromarketing outcomes that often derive from different combinations of causal conditions, and where factors productive of the same outcome may be different from those impeding it. In this paper we draw on set-theoretic theory and propose qualitative comparative analysis (QCA) as an analytical method able to complement and extend macromarketing research programs. Fuzzy-set QCA is used to explore combinations of conditions influencing COVID vaccine adoption, with readers provided with detailed guidance through the process and current best practices. We consider a number of important but often neglected issues in fuzzy-set QCA; outlining how to conduct robustness checks, appropriateness of a two-step approach, identifying individual cases with specific conditions for further analysis, and examining the problems and opportunities provided by irrelevant cases and contradictions. A summary of macromarketing issues that may benefit from QCA, and recommended practices for conducting a QCA, are provided.

Based on the configuration theory, this paper discusses the multiple concurrent causes and causal complex mechanisms affecting the performance differences among different new farmers. Using the fuzzy set qualitative comparative analysis method, taking 40 cases of CCTV’s “ZHI FU JING” column as samples, the paper analyzes the necessary conditions for new farmers to produce high performance by the anthefactory-variable configuration composed of human capital, social capital, psychological capital, entrepreneurial learning and entrepreneurial opportunity identification. The results show that: (1) high human capital is the necessary core condition for new farmers to produce high entrepreneurial performance, lack of high entrepreneurial learning and lack of high entrepreneurial opportunity identification is the core necessary condition for low entrepreneurial performance; (2) the driving mechanism of new farmers’ high entrepreneurial performance is divided into three paths, and the driving mechanism of new farmers’ low entrepreneurial performance is divided into two paths; and (3) the ways of inhibiting new farmers’ performance and promoting their performance are asymmetrical.

This article investigates the tradition of case-oriented configurational research, focusing specifically on qualitative comparative analysis (QCA) as a tool for causal inference. It first presents two analytic procedures commonly used by comparative researchers. A short description of the state-of-the-art of QCA applications is offered, in terms of discipline, types of cases, models, combinations with other methods, and software development. It then reviews different uses of QCA, as well as generic ‘best practices’. Some key recent evolutions are illustrated: on the one hand the development, beyond dichotomous ‘crisp set’ QCA (csQCA), of multi-value QCA (mvQCA), fuzzy sets, and fuzzy-set QCA (fsQCA), and on the other hand technical advances and refinements in the use of the techniques. Finally, the article gives some concluding reflections as to expected developments, upcoming innovations, remaining challenges, expansion of fields of application, and cross-fertilization with other approaches.

Purpose: This study investigated the simultaneous impact of conditions on employee's job satisfaction in Polish small and medium-sized enterprises (SMEs).
 Methodology: This study used the survey technique to better understand the determinants of job satisfaction the fuzzy set Qualitative Comparative Analysis (fs/QCA) was preferred. Qualitative comparative analysis (QCA) is a widely used method in the field of political science and sociology. In recent years, the use of the fuzzy set Qualitative Comparative Analysis (fs/QCA) in business and management research has also increased.
 Result: The results of our empirical study contribute to research on job satisfaction by presenting several conditions that create constellations affecting employee job satisfaction in Polish SMEs. The results certify previous research on employee satisfaction, exploring the important factors such as: organizational identification, co-workers support, rewards, supervisor relationship and quality of work life. It is worth noting that our research contributes to different constellations lead to job satisfaction by investigating the effect of all of selected conditions simultaneously.
 Applications: This finding can be useful for small and medium enterprises to enhance employee job satisfaction, which in turn translates into the results of the entire organization.
 Novelty/Originality: In this research, the model of conditions affecting employee job satisfaction in polish SMEs, a qualitative comparative analysis is presented in a comprehensive and complete manner.

Qualitative Comparative Analysis (QCA), a technique by which the tools of Boolean algebra are applied to equifinal causal conditions, is gaining popularity amongst scholars. This paper draws upon a distinction largely overlooked by the QCA literature: the difference between inclusive- and exclusive-or (OR and XOR). I argue that XOR should be included amongst the tools of QCA, explain why XOR is more easily applied to crisp- than fuzzy-set QCA, and provide two original techniques for applying XOR to fuzzy sets: mechanical and calibrated. With the calibrated technique, the application of the exclusive-or is related to substantive knowledge of the cases with two threshold values: (1) how large two fuzzy set values need to be in order to violate a prior commitment or overshoot a target outcome, and (2) how similar two values need to be in order to violate the rule: ‘A or B, but not both’. This paper improves the capacity of QCA expressions to mirror natural language closely, formalize conversational implicature, and deal with mutually exclusive clusters of sufficiency conditions. It includes a helpful step-by-step guide for QCA practitioners.

Qualitative comparative analysis, crisp and fuzzy sets in knowledge and innovation

ESG and financial performance: A qualitative comparative analysis in China's new energy companies

Beyond regression: Unpacking research of human complex systems with qualitative comparative analysis

Configurational theory in business and management research: Status quo and guidelines for the application of qualitative comparative analysis (QCA)

Set-theoretic methods and Qualitative Comparative Analysis (QCA) in particular are case-based methods. There are, however, only few guidelines on how to combine them with qualitative case studies. Contributing to the literature on multi-method research (MMR), we offer the first comprehensive elaboration of principles for the integration of QCA and case studies with a special focus on case selection. We show that QCA's reliance on set-relational causation in terms of necessity and sufficiency has important consequences for the choice of cases. Using real world data for both crisp-set and fuzzy-set QCA, we show what typical and deviant cases are in QCA-based MMR. In addition, we demonstrate how to select cases for comparative case studies aiming to discern causal mechanisms and address the puzzles behind deviant cases. Finally, we detail the implications of modifying the set-theoretic cross-case model in the light of case-study evidence. Following the principles developed in this article should increase the inferential leverage of set-theoretic MMR.