Guest editorial: Addressing organizational effectiveness with qualitative comparative analysis Free

Addressing complex phenomena in social sciences research entails the formulation of pertinent research questions, the provision of sound theoretical foundations, the application of appropriate research methods, and the delivery of strong and meaningful theoretical and practical contributions. Research methods serve as fundamental pillars for developing and testing theories (Du et al., 2025). Selecting appropriate methods is crucial to accurately capture and interpret the complex causal relationships that help to understand many social sciences phenomena. One of the key challenges for researchers is to identify appropriate analytical methods and integrate it effectively into the research process due to sample error and common method bias qualitative comparative analysis (QCA) is a case-based method that can address these issues.

QCA can be used to examine “how multiple conjunctural causation affects the conceptualization of social phenomena, thus contributing to a greater diversity in approaches to theorizing organizational and management phenomena” (Meuer and Fiss, 2020, p. 13 of 25). This method represents the emergence of a neo-configurational perspective in the study of management and organizations, and method enables a nuanced conceptualization and empirical examination of causal complexity through set theory (Misangyi et al., 2017, p. 1), as it explicitly establishes causal relationships across three lines of complexity: equifinality, conjunctural causation and causal asymmetry (Fiss, 2011; Ragin, 2008; Schneider and Wagemann, 2012). Indeed, until recently, there has been a lack of relevant tools, such as QCA, that can fully capture causal complexity in the form of these three features (Fiss et al., 2013). The appropriateness of QCA vis a vis other methods to better capture the complexity of social science phenomena has been widely recognized in several disciplines, including management (e.g. Di Paola et al., 2025; Furnari et al., 2021; Misangyi et al., 2017), information systems (e.g. Pappas and Woodside, 2021; Park et al., 2020), marketing (e.g. Saridakis et al., 2022; Woodside, 2013), international business (Fainshmidt et al., 2020), entrepreneurship (Du et al., 2025), operations management (e.g. Franke et al., 2024; Ketchen et al., 2022; Russo et al., 2019), strategy (Greckhamer et al., 2018), knowledge and innovation (Huarng and Roig-Tierno, 2016), human resources (Hauff et al., 2021; von Thiele Schwarz et al., 2024), accounting (e.g. Bedford and Sandelin, 2015) and leadership (McDermott, 2023), among others.

The diffusion of QCA has been accompanied by a number of papers that debate standards and good practices in QCA (Greckhamer et al., 2018; Pappas and Woodside, 2021; Schneider and Wagemann, 2010; Woodside, 2016), provide a step-by-step guide of QCA (Pappas and Woodside, 2021; Saridakis et al., 2022) and discuss the advantages of QCA against correlational techniques (Pappas and Woodside, 2021; Vis, 2012; Woodside, 2013, 2014). Illustrative examples of such advantages over traditional statistical methods are its potential use for theoretical progress and testing in management (Woodside, 2013), and the analysis of the conditions that lead to both the presence and the absence of the result (Fiss, 2011; Ragin, 2008). In addition, QCA can be applied in studies that use sample sizes ranging from less than 50 cases (very small samples) to thousands of cases (very large samples) (Pappas and Woodside, 2021). On one hand, when the sample size is small, it is possible to go back to the cases after the analysis and interpret them separately. On the other hand, for large samples, it allows to identify patterns across many cases without returning to the cases (Greckhamer et al., 2013). Despite these advantages, symmetrical methods such as multi-regression and structural equation modelling have been dominant in the literature. Empirical research on configurations remains limited (Saridakis et al., 2022).

QCA respects four foundational elements, namely: (1) viewing causality in terms of necessity and sufficiency between sets; (2) calibrating the membership of cases into sets; (3) conceptualizing cases as set theoretical configurations and (4) using counterfactual analysis to deal with unobserved configurations. QCA serves both as “an analytical technique and as a conceptual perspective” (Meuer and Fiss, 2020, p. 15 of 25). There are distinct variants of QCA (Thiem, 2014) like Crisp-set QCA, Fuzzy-set QCA (fsQCA) and multi-value QCA (mvQCA). Nevertheless, among the three variants of QCA, however, fsQCA is the one responsible for the largest amount of research (Roig-Tierno et al., 2017). By using fsQCA, researchers may be able to contribute in a different ways, as it offers several advantages compared to the most traditional methods, such as regression-based techniques (Berg-Schlosser et al., 2009; Woodside, 2013). Unlike these methods, QCA allows for conjunctural causation, equifinality and causal asymmetry, where the researcher is asked “not to specify a single causal model that fits the data best (as one usually does with statistical techniques), but instead to determine the number and character of the different causal models that exist among comparable cases” (Ragin, 2008, p. 167). QCA also differ from these methods in other ways: it is a case-oriented technique based on set theory, whereas correlational methods are rooted in correlations; it focuses on combinatorial effects rather than the net effect of each variable; traditional methods use raw data, whereas QCA transforms the raw data into a set of membership scores that can range from zero (full exclusion from a set) to one (full inclusion in a set); QCA does not follow some basic assumptions that underlie correlational methods, such as permanent causality, uniformity of causal effects, unit homogeneity, additivity and causal symmetry (Berg-Schlosser et al., 2009); QCA does not assume that data are drawn from a given probability distribution. QCA addresses key limitations of correlational methods such as the ability to account for complex interactions between variables (Fiss, 2011; Marx et al., 2014). Three-way interactions represent the boundaries of interpretable results, while configurations may well exceed the limit of three conditions.

Organizational effectiveness is a multifaceted and complex phenomenon that remains a critical concern for both academics and practitioners. As noted above, in recent years, QCA has emerged as a powerful analytical method capable of capturing complex causal relationships and uncovering configurational pathways to desired outcomes (and their absences). The main purpose of this special issue is to provide a platform for researchers to present novel theoretical insights and practical applications of QCA that contribute to a more nuanced understanding of organizational effectiveness at both the organizational and individual levels. Like any analytical method, QCA has its limitations. It is highly sensitive to case selection, causal conditions (Marx, 2010) and calibration thresholds (Fiss, 2011; Ragin, 2008; Schneider and Wagemann, 2012), among others. These challenges have sparked debates on various methodological concerns, including the role of control variables, case sensitivity, the use of logical remainders (Duşa, 2019), issues of temporality and the robustness of sensitivity analyses (e.g. Schneider and Wagemann, 2012; Thiem et al., 2016).

This special issue brings together studies that use QCA to explore different dimensions of organizational effectiveness, addressing performance issues at the individual level (knowledge sharing with co-workers, auditors’ job performance, employability skills perceptions, employees’ engagement in workplace romance, intrapreneurship and person-job fit, and employee motivation), and organization at the societal (policy) level (entrepreneurial innovation, and openness of strategic processes of public organizations) covering many angles that reveal the main advantages of this analytical method. The eight articles in this special issue illustrate the key role of the QCA method in advancing the understanding of non-linear and combinatory research. The articles gather contributions from researchers with different methodological, ontological and philosophical options. The studies deliver sound evidence and both original and cumulative results in previous literature that reassure us of the evident contribution of QCA, and the applied models, to explore complex phenomena. An overview of the included papers highlights key aspects such as the study’s background, anchor theory(ies), research design, sample, data collection and analysis, as well as the main findings and contributions. Most studies in this Special Issue use the more often applied variant of QCA: the fuzzy-set QCA (fsQCA). The studies address different units of analysis, use different theoretical lenses and, thus, contribute to better understanding of a variety of complex phenomena. Overview of these papers is as follows.

Baartmans et al. (2025) apply QCA to address the participation in the strategic processes of public organizations. Their study aims to enhance inclusion and transparency, thereby contributing to improved performance of public organizations. The research identified how public organizations can open up their strategic processes to external participants.

Reyes-Mercado and Larios Hernandez (2025) analyze the country-level causal configurations of digital enablers that result in entrepreneurial innovation in new ventures. They focus on the contextual role of information and communication technologies from an organizational center-edge approach. By integrating fsQCA and NCA the study offers a more nuanced understanding of context dependent factors that define entrepreneurial innovation in new ventures than fsQCA alone.

Chouchane and St-Jean (2025) determine the configurations of situations in which the person-intrapreneurship fit and psychosocial factors that lead to different types of motivation. The study offers a novel perspective on the role of person-job fit in the specific context of intrapreneurship, based on a new categorization of fit based on the disparity between employees’ intrapreneurial intention and actual intrapreneurial behavior.

Rudawska and Sławik (2025) examined the combinations of individual and relational factors that influence high- and low-intensity knowledge sharing among workers using the ability–motivation–opportunities (AMO) framework. The study provides configurations of conditions pertaining to abilities, motivation and opportunities that can facilitate or constrain knowledge-giving.

Hisa (2025) provides causal recipes among training competencies dimensions and self-esteem to predicting employability skills perceptions. The study examines the different combinations across specific demographic characteristics and professions.

Henriques and Samagaio (2025) analyze the influence of stress, job satisfaction and work–life balance on auditors’ job performance and examine the moderating effect of telework on previous relationships. The study results illustrate the importance of using a multi-method design for a better understanding of the complex structure of configurations that are sufficient for job performance.

Porfírio et al. (2025) focus on how organizational purpose can contribute to employee motivation, using personal and institutional factors and considering the importance of employee motivation toward organizational success. The study concludes that the need to share the organizational purpose through leadership actions and the importance of helping workers recognize the organizational purpose and its values, integrate them into their actions and feel how they contribute to its achievement.

Finally, Doll and Woodside (2025) apply complexity theory to propose and empirically examine asymmetric case conditions of antecedents and outcome models of high (low) willingness-to-engage in workplace romance. The study focuses on constructing complex antecedent conditions that accurately indicate which employees, and under what conditions, employees are high in willingness-to-engage in workplace romance, adding nuance to the current understanding of the behavior.

Table 1 provides an incisive summary of the articles.

This special issue provides evidence on the three key features of QCA. First, the articles in this special issue demonstrate the adaptability of the method to different types of data, including qualitative (e.g. interviews) and quantitative (e.g. survey data); primary (e.g. survey data, interviews) and secondary (e.g. World Economic Forum database); subjective (e.g. perceptions of employability skills) and objective (e.g. innovation metrics from the Global Entrepreneurship Monitor [GEM] database) data. In addition, the studies demonstrate the applicability of QCA across different sample sizes (e.g. 12 projects, 64 countries, 322 Portuguese auditors), research designs (e.g. qualitative, quantitative, experimental) and theoretical frameworks (e.g. Self-determination theory, job demands–resources theory, person-job fit theory, AMO theory, human capital theory). Second, the studies reveal the complementarity of QCA in conjunction with other analytical methods (e.g. necessary condition analysis [NCA], structural equation modeling [SEM] using partial least squares [PLS]) and the richness of the results it provides. Finally, the articles also demonstrate the potential of QCA in exploring the tenets of configurational theory (asymmetric causality, equifinality, multifinality, and conjunctural causation), leading to more nuanced interpretations of organizational phenomena. Consistent with the broader use of QCA in literature, most of the studies in this special issue employ the fsQCA variant. In addition, each study highlights the value of configuration analysis in identifying necessary and sufficient conditions for achieving specific organizational outcomes. Following best practices, the authors of some of the reported studies in this issue explore the possibility of uncovering the configurations leading to the outcomes of interest and their absences. In summary, the papers in this special issue benefit from diverse aspects of QCA across different dimensions of performance. Moreover, they contribute to an engaging discussion that advances both the literature on organizational effectiveness and the literature on QCA.

The increasing complexity of today’s business environment – driven by rapid advances in digital technologies, particularly artificial intelligence (AI), alongside sustainability imperatives, economic uncertainty and broader societal shifts – requires strategic redefinition for organizations to effectively address these challenges. Looking ahead, we encourage researchers to further explore the application of QCA in identifying alternative pathways to enhance organizational effectiveness under this complex context, providing valuable insights into how organizations can adapt and thrive in this evolving landscape. While QCA alone can effectively address different research objectives and answer specific research questions, we encourage researchers to adopt a mixed-methods approach, integrating QCA with other analytical methods to generate complementary insights. Several previous studies, including those presented in this special issue (e.g. Henriques and Samagaio, 2024; Reyes-Mercado and Hernandez, 2025), illustrate how QCA can be combined with methods such as necessary condition analysis (NCA) and structural equation modelling (SEM), demonstrating the methodological complementarity of these approaches in enhancing data interpretation and providing more precise insights into causal relationships. For example, the integration of NCA with fsQCA provides a deeper understanding of necessary conditions. While fsQCA assesses necessity in terms of type, NCA assesses it in terms of degree, i.e. NCA identifies the specific level of a condition required for a specific level of an outcome. The future is promising for the use of QCA because there are almost unlimited contributions that can be made due to the complex nature of organizational and societal phenomena. QCA can support very different rationales and explore very different types of data. Thus, we invite colleagues to continue to apply it, on behalf of meaningful contributions and impact both theory and practice.

We are grateful to all authors who contributed to this Special Issue and reviewers for their time, valuable feedback and expertise in enhancing the quality of these papers. Special thanks are dedicated to editorial staff of JOEPP for their assistance in producing this volume.