Research on the construction of techno-ethical order under the paradigm of risk society theory – an empirical study based on scientometrics and qualitative comparative analysis

We investigate the governance and environmental justice (EJ) outcomes from the hazard reclassification of ethylene oxide (EtO) by the Environmental Protection Agency in 2016. In response to EtO pollution after 2018, federal and state regulators engaged constituents to respond to complaints about EtO but adhered to a cost-benefit governance approach that ultimately inhibited risk mitigation. We argue risk mitigation was constrained by path dependent approaches that simultaneously enabled recognition of constituent concerns about EtO pollution while minimizing the costs of institutional change. Drawing on data from government documents and interviews, we analyze governance responses in Illinois and Georgia, selected due to their cross-cutting exposures to EtO and public mobilization in response to EtO risk. Our research reveals how structural and political factors limit risk mitigation and create a mismatch between environmental outcomes and public expectations. Drawing from theories of environmental justice and risk society, we show how this acceptance of EtO risk aligns with Ulrich Beck's theory of a risk society while generating significant justice concerns for its inability to consider how risk experiences vary according to social class.

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

ABSTRACTAimsThe study focused on nurses' familiarity with, beliefs about, and attitudes towards artificial intelligence, aiming to identify configurations of necessary and sufficient conditions associated with strong intentions to use artificial intelligence‐based health technologies in their clinical practice.DesignCross‐sectional survey conducted online from mid‐October 2023 through early February 2024.MethodsThe fuzzy set qualitative comparative analysis method was employed to analyse the survey data.Data Source307 members of the professional order of nurses in Québec province, Canada.ResultsFindings from the qualitative comparative analysis show that strong intentions to use artificial intelligence are only observed when nurses perceive artificial intelligence to have a high impactfulness on their future clinical practice (necessary condition). Moreover, we observe three configurations of sufficient conditions, that is, three combinations (artificial intelligence profiles) of familiarity with, belief about, trust in, and perceived impactfulness of artificial intelligence.ConclusionCurrent curriculum efforts have centred on defining artificial intelligence competencies, yet competency alone does not guarantee a willingness to adopt artificial intelligence tools. Our findings indicate that a positive attitude towards artificial intelligence's potential impact is crucial, with various profiles supporting intentions to adopt artificial intelligence.Implications for the ProfessionThese findings suggest that nurses' preparation should go beyond developing artificial intelligence competencies and that nursing educators and trainers need to account for the different profiles associated with strong intentions to use artificial intelligence technologies. Training programmes and nursing curricula should prioritise shaping nurses' beliefs and attitudes about artificial intelligence rather than focusing solely on technical skills.ImpactWe contribute to nursing research by showing that a positive attitude towards artificial intelligence's impactfulness on nurses' future clinical practice is a necessary condition for having high intentions to use artificial intelligence technologies.Reporting MethodRelevant guidelines have been adhered to by employing recommended qualitative comparative analysis reporting methods.Patient or Public ContributionNo patient or public contribution.

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.

In educational policy research, linking specific practices to specific outcomes is an important (though not the only) goal, which can bias researchers (and funders) toward employing purely quantitative methods. Given the context-specific nature of policy implementation in education, however, we argue that understanding how specific practices lead to specific outcomes in specific conditions or contexts is critical to improving education. Qualitative comparative analysis is a method of qualitative research that we argue can help to answer these kinds of questions in studies of educational policies and reforms. Qualitative comparative analysis is a case-oriented research method designed to identify causal relationships between variables and a particular outcome. Distinct from quantitative causal methods, qualitative comparative analysis requires qualitative data to identify conditions (and combinations of conditions) that lead to a particular result; it is context driven, just as many educational reforms must necessarily be. We contend that qualitative comparative analysis has the potential to be of use to educational researchers in investigating complex problems of cause and effect using qualitative data. As such, our aim here is to provide a general overview of the characteristics, processes, and outcomes of qualitative comparative analysis. In so doing, we hope to offer guidance to educational researchers around how and when to use qualitative comparative analysis, as well as recommendations for current educational issues that could be investigated with qualitative comparative analysis.

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).

Qualitative comparative analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. To explore the strengths and weaknesses of using QCA for HSR. Using data from semistructured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. Ninety administrative and clinical key informants interviewed across 10 NYC area safety net hospitals. Transcribed interviews were coded by 3 investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods.

BackgroundThe effectiveness of crisis response can be influenced by various structural, cultural, and functional aspects within a social system. This study uses a configurational approach to identify combinations of sociopolitical conditions that lead to a high case fatality rate (CFR) of COVID-19 in OECD countries.MethodsA Fuzzy set qualitative comparative analysis (QCA) is conducted on a sample of 38 OECD countries. The outcome to be explained is high COVID-19 CFR. The five potentially causal conditions are level of democracy, state capacity, trust in government, health expenditure per capita, and the median age of population. A comprehensive QCA robustness test protocol is applied, which includes sensitivity ranges, fit-oriented robustness, and case-oriented robustness tests.ResultsNone of the causal conditions in both the presence and negation form were found to be necessary for high or low levels of COVID-19 CFR. Two different combinations of sociopolitical conditions were usually sufficient for the occurrence of a high CFR of COVID-19 in OECD countries. Low state capacity and low trust in government are part of both recipes. The entire solution formula covers 84 percent of the outcome. Some countries have been identified as contradictory cases. The explanations for their COVID-19 CFR require more in-depth case studies.ConclusionsFrom a governance perspective, the weakness of government in effectively implementing policies, and the citizens’ lack of confidence in their government, combined with other structural conditions, serve as barriers to mounting an effective response to COVID-19. These findings can support the idea that the effects of social determinants of COVID-19 outcomes are interconnected and reinforcing.

In its original form, Qualitative Comparative Analysis (QCA) struggles with cases that evolve over time. Temporal QCA (TQCA) and Time-Series QCA (TS/QCA) are among the older variants that try to incorporate time in the comparative analysis. Recently, many more alternative strategies have been proposed. This article provides an overview of all known strategies to account for time and process in QCA, so that researchers can make informed research design choices. We present the main strategies regarding: the research aspect to which it pertains (casing, calibration, and/or truth table analysis), data requirements, the nature of the results, and the type of research questions that can be addressed. The following eight strategies are discussed: a conventional QCA in a mixed-methods design, using temporal conditions, conducting multiple QCA analyses for different time periods, conducting one QCA for different time periods, Trajectory-Based QCA (TJ-QCA), TQCA, TS/QCA, and Linear Growth QCA (LG-QCA).

The subject of this article is to discuss the cyclical effects of artificial intelligence (AI), a man-made technological system, on human, human consciousness and society. In light of risk society theory, the article's goal is to demonstrate how artificial intelligence, a technology created by humans, can control people and society and what kinds of problems it may pose. Examining the potential interconnections between an artificial entity and consciousness and a real entity (human/society) and consciousness is the article's primary issue. Methodologically, this study adopts descriptive approach. One of the most talked-about subjects in recent years is artificial intelligence, a technological system created by humans that seeks to comprehend how human intellect works and influences action. This technological system can create code, store large amounts of data and use algorithms to generate behavior and communicate in a manner similar to that of a human. AI technology is also capable of solving issues and carrying out a variety of human tasks. In this respect, it can be said that artificial intelligence acts as an artificial existence and consciousness. The theory of risk society, put forward by Ulrich Beck, in its most general definition, is that modern humans create various risks that will threaten their own existence with the knowledge, science and technology they produce. In other words, it means that man puts himself into a network of uncertainties and risks with what he produces in the cyclical world he lives in. Artificial entities and consciousness share the human ability to be a conscious entity, use reason, and generate ideas because artificial intelligence technology produces results that are almost identical to human intellect and consciousness. Although this situation seems to make human life easier, it may carry the risk of pacifying human qualities and devaluing people. Moreover, it can establish a kind of technological hegemony by putting people and societies under the control of technological devices. In line with these evaluations, the article first deals with risk society theory from a conceptual perspective. Then the relationship between technology, human and society is discussed. Finally, the framework of risk society theory is used to analyze the potential impacts of artificial intelligence technology on human life. According to the findings obtained from the article, artificial entity and consciousness based on artificial intelligence technology may blunt the ability to use human qualities and banalize many functions that make human entity special. Moreover, as an entity with reality on the axis of reasoning, thinking and producing solutions, artificial intelligence may fill the human field with the power of alternative and much faster analysis. As a result, it was understood in the study that the human-human relationship that shapes social life may evolve into a human-technology relationship in the future.

Qualitative Comparative Analysis (QCA) includes two main components: QCA “as a research approach” and QCA “as a method”. In this study, we focus on the former and, by means of the “interpretive spiral”, we critically look at the research process of QCA. We show how QCA as a research approach is composed of (1) an “analytical move”, where cases, conditions and outcome(s) are conceptualised in terms of sets, and (2) a “membership move”, where set membership values are qualitatively assigned by the researcher (i.e. calibration). Moreover, we show that QCA scholars have not sufficiently acknowledged the data generation process as a constituent research phase (or “move”) for the performance of QCA. This is particularly relevant when qualitative data–e.g. interviews, focus groups, documents–are used for subsequent analysis and calibration (i.e. analytical and membership moves). We call the qualitative data collection process “relational move” because, for data gathering, researchers establish the social relation “interview” with the study participants. By using examples from our own research, we show how a dialogical interviewing style can help researchers gain the in-depth knowledge necessary to meaningfully represent qualitative data into set membership values for QCA, hence improving our ability to account for the “qualitative” in QCA.

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.

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