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Shalev's (2007) critique of the use of multiple regression in comparative research brings together and synthesizes a variety of previous critiques, ranging from those focusing on foundational issues (e.g., the persistent problem of limited diversity), to estimation issues (e.g., the unrealistic assumption of correct model specification), to narrow technical issues (e.g., the difficulty of deriving valid standard errors for regression coefficients in pooled cross-sectional time-series models). Broadly speaking, these concerns can be described as epistemological, theoretical, and methodological, respectively. While the distinctions among these three are not always clear-cut, the tripartite scheme provides a useful way to map the different kinds of critiques that may be directed at the use of regression analysis in comparative research. In the first half of this essay we build upon Shalev's discussion to clarify the conditions under which regression analysis may be epistemologically, theoretically, or methodologically inappropriate for comparative research. Our goal is to situate Shalev's specific critiques of the use of multiple regression in comparative work within the context of social research in general.

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 aim of this chapter is to introduce a methodology that enables researchers to employ a set of systematic comparative tools and techniques in their multiple case study research that allow them to move from drawing loose comparisons towards a more formalised type of analysis, while simultaneously paying attention to within-case complexities. This methodology stands between the qualitative and the quantitative methods and helps researchers to build middle-range theories (Mjoset, 2001) from small to intermediate numbers of cases. This methodology encompasses a number of techniques including crisp and fuzzy set-theoretic qualitative comparative analyses, which have been used in a wide range of social science disciplines. However, these techniques have not received sufficient attention from higher education scholars.

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.

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.

This paper aims to describe how a fuzzy qualitative comparative analysis can be used to describe which combinations of academic factors are most influential for achieving success in college‐level mathematics. Using a fuzzy qualitative comparative analysis allows for the comparison of all possible combinations for a collection of predictor variables, as well as strategies for determining which configurations of these sets of variables are the most consistent with success in college‐level mathematics. Recent advances in fuzzy qualitative comparative analysis techniques have now integrated traditional qualitative comparative analysis strategies with formal statistical tests, thus allowing for the analysis and comparison of complex relationships that are difficult to describe with more traditional statistical methods such as regression analysis.

Data were collected from 259 full‐time, first‐time freshmen at a large state university in the USA. They were analysed using fuzzy‐set qualitative comparative analysis (FQCA).

Findings from this study suggest that the most parsimonious configuration of college remediation status, spending less time away from mathematics, and doing better in high school mathematics are key to success in college‐level mathematics.

Although numerous studies have made great progress in describing the complex relationship between prior mathematics exposure in high school with success in college‐level mathematics, one limitation of many studies is that they rely on analytic methods that only estimate the net effect of a single predictor variable, or a very small collection of predictor variables. This study utilises fuzzy‐set qualitative comparative analysis (FQCA) which can be used to analyze more complex interrelationships among a collection of predictor variables.

This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field.

This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct.

This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions.

The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.

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.

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.