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Purpose – This chapter examines the potential of qualitative comparative analysis (QCA) for strategy research.

Methodology/approach – We introduce the set-theoretic framework of QCA and provide an overview of recent methodological developments.

Findings – We utilize a variety of examples relevant to strategy research to illustrate the action steps and key concepts involved in conducting a QCA study.

Originality/value of paper – We develop examples from core research areas in strategic management to illustrate QCA's potential for examining issues of causality and diversity in strategy research, and in settings involving medium-N samples. We conclude by emphasizing that QCA offers an alternative mode of inquiry to open and redirect important lines of strategy research.

Research in entrepreneurship is increasingly exploring how archetypes, taxonomies, typologies, and configurations can help scholars understand complex entrepreneurial phenomena. We illustrate the potential for set-theoretic methods to inform this literature by offering best practices regarding how qualitative comparative analysis (QCA) can be used to explore research questions of interest to entrepreneurship scholars. Specifically, we introduce QCA, document how this approach has been used in management research, and provide step-by-step guidance to empower scholars to use this family of methods. We put a particular emphasis on the analytical procedures and offer solutions to dealing with potential pitfalls when using QCA-based methods and highlight opportunities for future entrepreneurship research.

Qualitative Comparative Analysis (QCA) is a promising, powerful method that is increasingly used for IS research. However, the Information Systems (IS) discipline still lacks a shared understanding of how to conduct and report QCA. This paper introduces the fundamental concepts of QCA, summarizes the status quo, and derives recommendations for future research.

A descriptive literature review in major IS outlets summarizes how and why QCA has been used in the IS discipline, critically evaluates the status quo, and derives recommendations for future QCA studies.

The literature review reveals 32 empirical research articles in major IS journals that have used the QCA method. Articles applied QCA to a broad range of research topics at the individual and organizational levels, mainly as a standalone analysis for theory development, elaboration and testing. The authors also provide evidence that most published IS research articles do not take full advantage of the potential QCA, such as analyzing necessary causal conditions or testing the robustness of QCA results. The authors provide seven actionable recommendations for future IS research using QCA.

The literature review assesses the status quo of QCA’s application in the IS discipline and provides specific recommendations on how IS researchers can leverage the full potential of QCA.

The aim of this chapter is to review and illustrate a step-by-step guideline in conducting fuzzy-set Qualitative Comparative Analysis (fsQCA) in tourism and hospitality studies. As an emerging method, fsQCA is simultaneously quantitative and qualitative in nature which makes it an appropriate method for social science disciplines including tourism and hospitality area because of complex nature of relationships between multiple variables where theories and models are underdeveloped. Unlike conventional statistical techniques, fsQCA is an asymmetrical analysis technique that provides a holistic view and interrelationships among several conditions using Boolean algebra. The fsQCA analyses produce comprehensive assessment by revealing causal combinations of antecedents to predict an outcome; and identify sufficient configurations (i.e., causal combinations and recipes) and necessary condition/s. By utilizing this method, researchers would be able to produce complex, comprehensive, and robust results.

The purpose of this paper is to provide comprehensive mapping of qualitative comparative analysis (QCA) applications in business and management research and to examine the sub-fields of corporate governance research in this context.

Through a systematic literature review of 22 articles, the paper describes and analyses how QCA is used in the corporate governance field, what can be learned from the methodology’s implementation in corporate governance studies and why authors justify its use.

The findings highlight that QCA in corporate governance is still at an early stage of development. The paper encourages governance scholars to use this method to transform QCA from a niche into a mainstream method because it is appropriate for understanding both complex phenomena of social reality and issues of corporate governance that require an approach able to capture configurations of conditions, asymmetric patterns and equifinal explanations.

This is the first complete overview of the existing literature concerning QCA’s application in corporate governance research and reveals implications for its future use. In this way, it extends the previous work on QCA’s benefits to management researchers and other critical reviews of applications in QCA. This study encourages scholars to renew their understanding of corporate governance issues through a new analysis method that can help to discover conceptual and empirical relations among case-oriented and variable-oriented analyses in terms of interrelations to examine corporate governance practices holistically.

The purpose of this paper is to provide meaningful information about sponsorship management in state-owned enterprises.

Qualitative and quantitative data from Germany are analyzed in a case study approach using fuzzy-set qualitative comparative analysis (Fs/QCA)—an analytic method relevant for describing configurational patterns of causal factors.

The case study of sponsorships from state-owned enterprises in Germany reveals four alternative configurations of top-management support, sponsee prominence, standardized processes, and sponsorship leverage explaining sponsor satisfaction.

The paper combines two underrepresented but important aspects of sponsorship research, i.e. sponsorship management in state-owned enterprises, in an empirical study. Further, present study adds to sponsorship literature by pointing to fuzzy-set Fs/QCA as a relatively novel method that can capture the phenomenon of complex causality.

Because family businesses are highly complex enterprises, researchers need appropriate theoretical and methodological tools to study them. The neoconfigurational perspective and its accompanying method, qualitative comparative analysis, are particularly well suited to phenomena characterized by complex causality, but their uptake in family business research has been slow and fragmented. To remedy this, the authors highlight their unique ability to address research questions for which other approaches are not well suited and discuss how they might be applied to family business phenomena.

The authors introduce the core tenets of the neoconfigurational perspective and how its set-theoretic epistemology differs from traditional approaches to theorizing and analysis. The authors then use a dataset of family firms to present a primer on conducting qualitative comparative analysis and interpreting the results.

The authors find that family firm resources can be combined in multiple ways to affect business survival, suggesting that resources are substitutable and complementary. The authors discuss how the unique features of the neoconfigurational approach, namely equifinality, conjunctural causation and causal asymmetry, can be fruitfully applied to break new ground in scholarly understanding of family businesses.

This article allows family business researchers to apply the neoconfigurational approach without first having to consult multiple and disparate sources often written for other disciplines. This article explicates how to leverage the theoretical and empirical advantages of the neoconfigurational approach in the context of family businesses, supporting a more widespread adoption of the neoconfigurational perspective in family business research.

The purpose of this paper is to explore the configuration of factors affecting the accuracy of triage decision-making. The contribution of the work is twofold: first, it develops a protocol for applying a fuzzy-set qualitative comparative analysis (fsQCA) in the context of triage decision-making, and second, it studies, through two pilot cases, the interplay between individual and organizational factors in determining the emergence of errors in different decisional situations.

The methodology adopted in this paper is the qualitative comparative analysis (QCA). The fuzzy-set variant of QCA (fsQCA) is implemented. The data set has been collected during field research carried out in the Emergency Departments (EDs) of two Italian public hospitals.

The results of this study show that the interplay between individual and contextual/organizational factors determines the emergence of errors in triage assessment. Furthermore, there are some regularities in the patterns discovered in each of the investigated organizational contexts. These findings suggest that we should avoid isolating individual factors from the context in which nurses make their decisions.

Previous research on triage has mainly explored the impact of homogeneous groups of factors on the accuracy of the triage process, without considering the complexity of the phenomenon under investigation. This study outlines the need to consider the not-linear relationships among different factors in the study of triage’s decision-making. The definition and implementation of a protocol to apply fsQCA to the triage process in EDs further contributes to the originality of the research.

Although QCA was originally developed specifically for small-N settings, recent studies have shown its potential for large-N organization studies. In this chapter, we provide guidance to prospective researchers with the goal of opening up QCA’s potential for widespread use in organization studies involving large-N settings, both as an alternative and as a complement to conventional regression analyses. We compare small-N and large-N QCA with respect to theoretical assumptions and objectives, processes and decisions involved in building the causal model, selecting the sample, as well as analyzing the data and interpreting the results. Finally, we discuss the prospects for large-N configurational analysis in organization studies and related fields going forward.

The purpose of this research is to examine why communities facing the same disaster recover differentially and determine pathways to successful disaster recovery in the research setting of New Orleans neighborhoods affected by Hurricane Katrina. While previous studies suggest that there are a variety of pathways to recovery, a broader cross-case comparison is necessary to generalize these pathways into a recovery framework. Specifically, this study seeks to determine what pre-disaster and post-disaster causal factors, alone or in combination, were important to recovery following Hurricane Katrina.

This paper presents a cross-case comparative study of neighborhood-level recovery. Based on prior work, which used the Delphi method to determine hypothesized causal factors and indicators of recovery, data was collected through publically available sources, including the US Census, the Greater New Orleans Community Data Center and previously completed studies for 18 damaged neighborhoods. Fuzzy-set qualitative comparative analysis was used due to its ability to analyze both quantitative and qualitative data for smaller case studies.

The results show that there are multiple pathways combining pre-disaster community factors and post-disaster actions that led to recovery, as measured by population return. For example, economic capacity is nearly sufficient for recovery, but a combination of low social vulnerability, post-disaster community participation, a high proportion of pre-World War II housing stock and high amounts of post-disaster funds also led to recovery.

This research uses a novel method to link pre-disaster measures of resilience and vulnerability to recovery outcomes and, through cross-case comparison, generates results that will enable researchers to develop a theory of sustainable community recovery.