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

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.

The configuration perspective in business management research is growing, but the lack of supportive research method slows it down. Since Fiss（2007）proposed to use the qualitative comparative analysis（QCA）to solve the problem of empirical research from the configuration perspective, the method has been widely used in management research overseas. Conversely, QCA is still in its initial stage with few achievements in China. Based on a systematic review of related literature adopting QCA in the management field at home and abroad, this paper summarizes that three major problems can be solved in management configuration research by QCA: exploring multiple routes resulting in the equifinality, dealing with complex interactions between antecedents and deepening & supplementing classification. Secondly, this paper summarizes QCA’s six advantages, including the low demand of quantity and sources of data, cause complexity, casual asymmetry, no need to deal with multilevel variables, the reduction in phenomenon complexity and complete case explanation. Facing the most questioned robustness in the application of QCA, this paper sums up a number of specific coping strategies. Finally, supported by the latest development of QCA, this paper proposes a three-dimensional research framework consisting of digging the past research, and cultivating the future research and time-series research.

Qualitative Comparative Analysis (QCA) is a powerful method originating in the fields of political science and sociology, where it is becoming a mainstream method. This article analyzes the state of QCA applications in business and management (B&M) research by conducting a structured literature review, which results in the identification of 96 studies between 1995 and 2015. Additionally, the knowledge basis of those articles is analyzed by means of a citations analysis. The 5,141 unique citations serve to also structure the research front using a bibliometric coupling analysis. The results point towards a somewhat deferred development of QCA in the discipline, which has recently undergone a quantum leap with regard to the number of publications as well as the advance of the method application. The current development is strongly determined by the originator of the method, Charles Ragin, and by the first studies applying QCA in business and management. Yet, the research front is only loosely connected, underlining that QCA remains at an early stage of adoption in business and management. The chapter gives three recommendations for future QCA studies and predicts a progressing profile formation of QCA in business and management research that can contribute to the adoption of configurational thinking within the discipline.

Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables

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.

BackgroundSystematic reviews evaluating complex interventions often encounter substantial clinical heterogeneity in intervention components and implementation features making synthesis challenging. Qualitative comparative analysis (QCA) is a non-probabilistic method that uses mathematical set theory to study complex phenomena; it has been proposed as a potential method to complement traditional evidence synthesis in reviews of complex interventions to identify key intervention components or implementation features that might explain effectiveness or ineffectiveness. The objective of this study was to describe our approach in detail and examine the suitability of using QCA within the context of a systematic review.MethodsWe used data from a completed systematic review of behavioral interventions to improve medication adherence to conduct two substantive analyses using QCA. The first analysis sought to identify combinations of nine behavior change techniques/components (BCTs) found among effective interventions, and the second analysis sought to identify combinations of five implementation features (e.g., agent, target, mode, time span, exposure) found among effective interventions. For each substantive analysis, we reframed the review’s research questions to be designed for use with QCA, calibrated sets (i.e., transformed raw data into data used in analysis), and identified the necessary and/or sufficient combinations of BCTs and implementation features found in effective interventions.ResultsOur application of QCA for each substantive analysis is described in detail. We extended the original review findings by identifying seven combinations of BCTs and four combinations of implementation features that were sufficient for improving adherence. We found reasonable alignment between several systematic review steps and processes used in QCA except that typical approaches to study abstraction for some intervention components and features did not support a robust calibration for QCA.ConclusionsQCA was suitable for use within a systematic review of medication adherence interventions and offered insights beyond the single dimension stratifications used in the original completed review. Future prospective use of QCA during a review is needed to determine the optimal way to efficiently integrate QCA into existing approaches to evidence synthesis of complex interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0256-y) contains supplementary material, which is available to authorized users.

Purpose The purpose of this paper is to critically reflect and offer insights on how to justify the use of qualitative comparative analysis (QCA) as a research method for understanding the complexity of organizational phenomena, by applying the principles of the neo-configurational approach. Design/methodology/approach We present and critically examine three arguments regarding the use of QCA for management research. First, they discuss the need to assume configurational theories to build and empirically test a causal model of interest. Second, we explain how the three principles of causal complexity are assumed during the process of conducting QCA-based studies. Third, we elaborate on the importance of case knowledge when selecting the data for the analysis and when interpreting the results. Findings We argue that it is important to reflect on these arguments to have an appropriate research design. In the true spirit of the configurational approach, we contend that the three arguments presented are necessary; however, each argument is insufficient to warrant a QCA research design. Originality/value This paper contributes to management research by offering key arguments on how to justify the use of QCA-based studies in future research endeavors.

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.

Qualitative Comparative Analysis (QCA) is a comparative tool that uses Boolean algebra for a systematic analysis of similarities and differences across cases. Most commonly, it is used causally in macrosocial study to investigate under which conditions a state of affairs is realized. Nevertheless, although not commonly recognized, the method has no in-built premises validating only causal applications. Here, we apply QCA to microsocial interview data on 14 teenagers who reflect upon themselves and the world through artistic practices. Our use of QCA is a process of categorization where several analyses, at various levels, follow each other, helping us to look at the cases from different angles and accordingly arrive at new ideas about their interrelations. The use of QCA forced us to penetrate deep into the principles of interpreting and organizing data, which we consider a major benefit of the method.

Qualitative Comparative Analysis (QCA) is a comparative tool that uses Boolean algebra for a systematic analysis of similarities and differences across cases. Most commonly, it is used causally in macrosocial study to investigate under which conditions a state of affairs is realized. Nevertheless, although not commonly recognized, the method has no in-built premises validating only causal applications. Here, we apply QCA to microsocial interview data on 14 teenagers who reflect upon themselves and the world through artistic practices. Our use of QCA is a process of categorization where several analyses, at various levels, follow each other, helping us to look at the cases from different angles and accordingly arrive at new ideas about their interrelations. The use of QCA forced us to penetrate deep into the principles of interpreting and organizing data, which we consider a major benefit of the method.

Qualitative comparative analysis (QCA) appears to offer a systematic means for case-oriented analysis. The method not only offers to provide a standardized procedure for qualitative research but also serves, to some, as an instantiation of deterministic methods. Others, however, contest QCA because of its deterministic lineage. Multiple other issues surrounding QCA, such as its response to measurement error and its ability to ascertain asymmetric causality, are also matters of interest. Existing research has demonstrated the use of QCA on real data, but such data do not allow one to establish the method’s efficacy, because the true causes of real social phenomena are always contestable. In response, the authors analyze several simulated data sets for which true causal processes are known. They find that QCA finds the correct causal story only 3 times across 70 different solutions, and even these rare successes, on closer examination, actually reveal additional fundamental problems with the method. Further epistemological analyses of the results find key problems with QCA’s stated epistemology, and results indicate that QCA fails even when its stated epistemological claims are ontologically accurate. Thus, the authors conclude that analysts should reject both QCA and its epistemological justifications in favor of existing effective methods and epistemologies for qualitative research.

In introducing the 2020 Emerging Discourse Incubator, Flynn et al. (2020, https://doi.org/10.1111/jscm.12227) urged supply chain scholars to leverage fresh approaches in order to develop supply chain‐specific theory, including approaches that are underutilized within the discipline. In response, we explain how more examination of configurations—meaningful sets of observations within a sample—can enhance theory development and, in particular, fuel the construction of supply chain‐specific theory. First, we describe the value of configurational theorizing while contrasting it with two more popular approaches: one that centers on linear relationships and one that spotlights the unique features of individual observations. Second, we explain the main configurational approaches available to scholars. Here, we pay special attention to qualitative comparative analysis (QCA)—an approach to configurational theorizing that is relatively new to organizational research. Third, we offer examples of how configurational theorizing via the use of QCA can be used to develop supply chain management theory. Although QCA is employed regularly in neighboring fields, QCA remains something of a conceptual curiosity within supply chain management research. This state of affairs represents an important opportunity because QCA's emphasis on causal complexity fits well with the fact that supply chain outcomes usually arise from an array of variables—often at different levels of analysis—and the interplay among them. Thus, better leveraging configurational theory development can facilitate the creation of novel conceptualizations and useful advice for practice.

Qualitative comparative analysis (QCA) is in the spotlight as a new analytical method that overcomes the problems of traditional quantitative and qualitative research and bridges the gap between them. The purpose of study is to advance the understanding of QCA and its potential applications in future research. The problems associated with traditional quantitative and qualitative research methods are presented, as well as a comparative overview of the characteristics and tasks of QCA; the application of QCA in administrative science and the attributes of QCA, which facilitate the analysis of phenomena characterized by causal asymmetry, conjunction and equifinality. The article examines how well QCA is suitable for administrative science. The usefulness of QCA in management research and the possible direction of future application is demonstrated.

BackgroundThere is an increasing recognition that health intervention research requires methods and approaches that can engage with the complexity of systems, interventions, and the relations between systems and interventions. One approach which shows promise to this end is qualitative comparative analysis (QCA), which examines casual complexity across a medium to large number of cases (between 10 and 60+), whilst also being able to generalise across those cases. Increasingly, QCA is being adopted in public health intervention research. However, there is a limited understanding of how it is being adopted. This systematic review will address this gap, examining how it is being used to understand complex causation; for what settings, populations and interventions; and with which datasets to describe cases.MethodsWe will include published and peer-reviewed studies of any public health intervention where the effects on population health, health equity, or intervention uptake are being evaluated. Electronic searches of PubMed, Scopus, Web of Science (incorporating Social Sciences Citation Index and Arts & Humanities Citation Index), Microsoft Academic, and Google Scholar will be performed. This will be supplemented with reference citation tracking and personal contact with experts to identify any additional published studies. Search results will be single screened, with machine learning used to check these results, acting as a ‘second screener’. Any disagreement will be resolved through discussion. Data will be extracted from full texts of eligible studies, which will be assessed against inclusion criteria, and synthesised narratively, using thematic synthesis methods.DiscussionThis systematic review will provide an important map of the increasing use of QCA in public health intervention literature. This review will identify the current scope of research in this area, as well as assessing claims about the utility of the method for addressing complex causation in public health research. We will identify implications for better reporting of QCA methods in public health research and for reporting of case studies such that they can be used in future QCA studies.Systematic review registrationPROSPERO, CRD42019131910

BackgroundQualitative Comparative Analysis (QCA) is a method for identifying the configurations of conditions that lead to specific outcomes. Given its potential for providing evidence of causality in complex systems, QCA is increasingly used in evaluative research to examine the uptake or impacts of public health interventions. We map this emerging field, assessing the strengths and weaknesses of QCA approaches identified in published studies, and identify implications for future research and reporting.MethodsPubMed, Scopus and Web of Science were systematically searched for peer-reviewed studies published in English up to December 2019 that had used QCA methods to identify the conditions associated with the uptake and/or effectiveness of interventions for public health. Data relating to the interventions studied (settings/level of intervention/populations), methods (type of QCA, case level, source of data, other methods used) and reported strengths and weaknesses of QCA were extracted and synthesised narratively.ResultsThe search identified 1384 papers, of which 27 (describing 26 studies) met the inclusion criteria. Interventions evaluated ranged across: nutrition/obesity (n = 8); physical activity (n = 4); health inequalities (n = 3); mental health (n = 2); community engagement (n = 3); chronic condition management (n = 3); vaccine adoption or implementation (n = 2); programme implementation (n = 3); breastfeeding (n = 2), and general population health (n = 1). The majority of studies (n = 24) were of interventions solely or predominantly in high income countries. Key strengths reported were that QCA provides a method for addressing causal complexity; and that it provides a systematic approach for understanding the mechanisms at work in implementation across contexts. Weaknesses reported related to data availability limitations, especially on ineffective interventions. The majority of papers demonstrated good knowledge of cases, and justification of case selection, but other criteria of methodological quality were less comprehensively met.ConclusionQCA is a promising approach for addressing the role of context in complex interventions, and for identifying causal configurations of conditions that predict implementation and/or outcomes when there is sufficiently detailed understanding of a series of comparable cases. As the use of QCA in evaluative health research increases, there may be a need to develop advice for public health researchers and journals on minimum criteria for quality and reporting.