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

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).

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.

Qualitative Comparative Analysis (QCA) is increasingly establishing itself as a method in social research. QCA is a set-theoretic, truth-table-based method that identifies complex combinations of conditions (configurations) that are necessary and/or sufficient for an outcome. An advantage of QCA is that it models the complexity of social phenomena by accounting for conjunctural, asymmetric, and equifinal patterns. Accordingly, the method does not assume isolated net effects of single variables but recognizes that the effect of a single condition (that is, an explanatory factor) often unfolds only in combination with other conditions. Moreover, QCA acknowledges that the occurrence of a phenomenon can have a different explanation from its non-occurrence. Finally, QCA allows for different, mutually non-exclusive explanations of the same phenomenon. QCA is not only a technique; there is a diversity of approaches to how it can be implemented before, during and after the “technical moment,” depending on the analytic goals related to contributing to theory, engaging with cases, and the approach to explanation. Particularly since 2012, an increasing number of scholars have turned to using QCA to investigate public administrations. Even though the boundaries of Public Administration (PA) as an academic discipline are difficult to determine, it can be defined as an intellectual forum for those who want to understand both public administrations as organizations and their relationships to political, economic, and societal actors—especially in the adoption and implementation of public policies. Owing to its fragmented nature, there has been a long-lasting debate about the methodological sophistication and appropriateness of different comparative methods. In particular, the high complexity and strong context dependencies of causal patterns challenge theory-building and empirical analysis in Public Administration. Moreover, administrative settings are often characterized by relatively low numbers of cases for comparison, as well as strongly multilevel empirical settings. QCA as a technique allows for context-sensitive analyses that take into account this complexity. Against this background, it is not surprising that applications of QCA have become more widespread among scholars of Public Administration. A systematic review of articles using QCA published in the major Public Administration journals shows that the use of QCA started in mid-2000s and then grew exponentially. The review shows that, especially in two thematic areas, QCA has high analytical value and may (alongside traditional methodological approaches) help improve theories and methods of PA. The first area is the study of organizational decision-making and the role of bureaucrats during the adoption and implementation of public policies and service delivery. The second area where QCA has great merits is in explaining different features of public organizations. Especially in evaluation research where the aim is to investigate performance of various kinds (especially effectiveness in terms of both policy and management), QCA is a useful analytical tool to model these highly context-dependent relationships. The QCA method is constantly evolving. The development of good practices for different QCA approaches as well as several methodological innovations and software improvements increases its potential benefits for the future of Public Administration research.

Qualitative Comparative Analysis (QCA) is a method, developed by the American social scientist Charles C. Ragin since the 1980s, which has had since then great and ever-increasing success in research applications in various political science subdisciplines and teaching programs. It counts as a broadly recognized addition to the methodological spectrum of political science. QCA is based on set theory. Set theory models “if … then” hypotheses in a way that they can be interpreted as sufficient or necessary conditions. QCA differentiates between crisp sets in which cases can only be full members or not, while fuzzy sets allow for degrees of membership. With fuzzy sets it is, for example, possible to distinguish highly developed democracies from less developed democracies that, nevertheless, are rather democracies than not. This means that fuzzy sets account for differences in degree without giving up the differences in kind. In the end, QCA produces configurational statements that acknowledge that conditions usually appear in conjunction and that there can be more than one conjunction that implies an outcome (equifinality). There is a strong emphasis on a case-oriented perspective. QCA is usually (but not exclusively) applied in y-centered research designs. A standardized algorithm has been developed and implemented in various software packages that takes into account the complexity of the social world surrounding us, also acknowledging the fact that not every theoretically possible variation of explanatory factors also exists empirically. Parameters of fit, such as consistency and coverage, help to evaluate how well the chosen explanatory factors account for the outcome to be explained. There is also a range of graphical tools that help to illustrate the results of a QCA. Set theory goes well beyond an application in QCA, but QCA is certainly its most prominent variant. There is a very lively QCA community that currently deals with the following aspects: the establishment of a code of standards for QCA applications; QCA as part of mixed-methods designs, such as combinations of QCA and statistical analyses, or a sequence of QCA and (comparative) case studies (via, e.g., process tracing); the inclusion of time aspects into QCA; Coincidence Analysis (CNA, where an a priori decision on which is the explanatory factor and which the condition is not taken) as an alternative to the use of the Quine-McCluskey algorithm; the stability of results; the software development; and the more general question whether QCA development activities should rather target research design or technical issues. From this, a methodological agenda can be derived that asks for the relationship between QCA and quantitative techniques, case study methods, and interpretive methods, but also for increased efforts in reaching a shared understanding of the mission of QCA.

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.

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.

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.

BackgroundThe sudden outbreak of COVID-19 in early 2020 pushed the online health-care communities (OHCs) into the public eye in China. However, OHCs is an emerging service model, which still has many problems such as low patient trust and low patient utilization rate. Patients are the users and recipients of web-based medical services, as well as the core of medical services. Thus, based on cue utilization theory, this paper studies combination effect of influencing factors in patients’ purchase of web-based medical services through the qualitative comparative analysis method of fuzzy sets (fsQCA).MethodsThis paper discards statistical methods based on variance theory-based relationships between explanatory and explained variables and uses a construct theory-based fuzzy set qualitative comparative analysis (fsQCA) approach to elucidate such complex relationships of patients' online purchasing behavior. We use a crawler to automatically download information from Haodf.com. This study crawled data in August 2020, involving 1210 physicians.ResultsService price, reputation and service quality are the key factors for patients’ purchasing behavior. Physician’s online reputation, online medical service price, number of published articles, mutual-help group, and appointment registration affect patients' purchasing behavior by means of weighted variation. Only when a high scope of internal attribute-related cue elements and a low scope of external attribute-related cue elements are combined with each other in a specific form, patients will generate purchase behavior.ConclusionThis paper clarifies the complex causes that promote to patients' purchasing behavior of web-based medical services, enriches and develops the relevant theories in the field of consumer purchasing behavior and online health-care communities market research, and has implications for governments, platforms, physicians and patients in the event of web-based medical service purchases.

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.

As the world’s largest payments for ecosystem services (PES) program, China’s Sloping Land Conversion Program (SLCP) is designed to combat soil erosion and land degradation by converting cropland on steep slopes into forests. Operating through an incentive-based approach, the SLCP involved 32 million rural households as core agents. This paper aims to fill a research gap regarding how socioeconomic and institutional conditions influence rural households to reach the primary environmental goals. Using fuzzy-set qualitative comparative analysis (fsQCA), we conclude that at the household level, the different pathways to environmental success or failure have been shaped by socioeconomic and institutional conditions in a combinatory manner rather than single conditions alone. Specifically, the combination of household involvement and effective monitoring plays a fundamental role in capacity-building between government and households. We found that financial incentives have a trade-off effect, as they could not only create a positive interaction but also trigger failure in situations with different conditions. Finally, the potential and limits of QCA were discussed, and we call for a more serious reflection on the added value of QCA as an alternative or complementary method to conventional approaches in environmental governance research.

BackgroundQualitative Comparative Analysis (QCA) is a method for examining configurational causality by identifying pathways that lead to an outcome of interest. There is a growing body of literature that uses QCA to measure child well-being due to its ability to generate evidence of causality for complex social phenomena. This scoping review examines how QCA studies are being employed to investigate child well-being and assesses the potential of QCA as a method to produce intervention-focused evidence and to contribute to future methodological development to address the complexity of child well-being.MethodWe systematically searched Embase, PsyINFO, MEDLINE, Social Policy and Practice, Global Health, Econlit, Scopus and Web of Science for peer-reviewed studies that had used QCA methods in child well-being studies. We searched studies published in English up until 2023. Systematic reviews and meta-analyses using QCA were excluded due to insufficient methodological detail for inclusion in our analysis. We followed the PRISMA-ScR flowchart and guidelines for study screening to ensure a systematic selection process. Data extraction was undertaken to capture information of most relevance to QCA best practice. Data were analysed using a basic qualitative content analysis approach.ResultsThe search identified 626 papers, of which 28 met our inclusion criteria. Dimensions of well-being included: psychological/mental health (n = 9); physical health (n = 2); language development under education (n = 1); socio-emotional health (n = 7); physical and psychological/mental health (n = 3), psychological/mental health and education (n = 1); and multi-dimensional health (n = 3). Two studies stated explicitly that they used well-being concepts—subjective well-being and psychological well-being. Most studies (n = 23) were predominantly in high income countries (HIC). Commonly reported strengths of QCA were the capacity to a) describe various pathways or combinations of pathways to the same outcome (equifinality); and b) examine conjunctural causation (combination of absent/present conditions), known as ‘causal complexity’. Weaknesses related to a) generalisability of the data; and b) the number of causal conditions that can be included in the analysis. Our findings suggest that QCA can be effectively used alongside traditional analyses to provide a more nuanced understanding.ConclusionQCA is a promising method with potential to address complexity when assessing the different dimensions of child well-being. More comprehensive guidelines are now available that offer good practices to enhance the quality of the QCA research. To build greater confidence using this method, scholars are recommended to adhere to these good practices to establish the highest levels of transparency of the analysis.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-23821-x.

Contents: Empirical QCA Studies - The model specification of Qualitative Comparative Analysis (QCA) in large-N designs - An innovative formula for QCA - The mismatch between hypotheses and method - The polarities and similarities of QCA and regression - An illustrative study using fuzzy set QCA (fsQCA) - Configurational theories in management studies.

Abstract. Qualitative Comparative Analysis (QCA) overlaps logistic regression in explaining events, but challenges the latter's lack of accounting for causal complexity. QCA has only to a limited degree been applied to large‐N studies or individuals as cases and has not incorporated the logic of probability. QCA and logistic regression are compared with respect to logic, procedure and outcome. Political orientations from five national surveys are adapted to the requirements of the two methods. The methods are demonstrated on explanations of individuals' party preferences. QCA and logistic regression converge and overlap in identifying degrees of causal complexity, in ascertaining model significance and in identifying antecedents to party preference. Results differ in degree, not in kind. A slightly more nuanced picture emerges using the QCA approach, whereas logistic regression delivers greater parsimony. Choice of method(s) is not arbitrary. QCA can easily be used on any large‐N research problem. It should apply probability when appropriate.

Qualitative Comparative Analysis (QCA) is a descriptive research method that can provide causal explanations for an outcome of interest. Despite extensive quantitative assessments of the method, my objective is to contribute to the scholarly discussion with insights constructed through a qualitative lens. Researchers using the QCA approach have less ability to incorporate and nuance information on set membership as the number of cases grows. While recognizing the suggested ways to overcome such challenges, I argue that since setting criteria for membership, calibrating, and categorizing are crucial QCA aspects that require in-depth knowledge, QCA is unfit for larger-N studies. Additionally, I also discuss that while the method is able to identify various parts of a causal configuration—the ‘what’—it falls short to shed light on the ‘how’ and ‘why,’ especially when temporality matters. Researchers can complement it with other methods, such as process tracing and case studies, to fill in these missing explanatory pieces or clarify contradictions—which begs the question of why they would also choose to use QCA.

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