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Most micro‐oriented research has assumed that leaders operate within a formally designed, pre‐specified and stable structural hierarchy. In the emerging knowledge economy, however, the watchwords have become flexibility and speed. Today's business practices feature temporary task forces, project teams, joint ventures, and alliances. The authors aim to draw from the political science literature, presenting methodology for examining team leadership in context that augments and enhances the analyses that can be obtained with commonly used correlational/regression models.

More specifically, the authors will develop a detailed analysis of the potential applicability of the set‐theoretic approach for elucidating a configural analysis of team leadership, demonstrating that this method provides insight into the data that complements that provided by regression analysis.

The authors examine the findings from more traditional approaches to analysis, and set‐theoretic analysis, and demonstrate that set‐theoretic analysis provides additional insights into the phenomena in question.

The possibilities for future research in the organizational leadership literature using set‐theoretic analysis are endless. The method is appropriate for theory development, theory confirmation, exploration, and more. Our current theoretical models are lacking in complexity, partly due to the limitations of existing methodology, and our advice to organizations would be significantly enriched were we able to establish that there are alternate means to the desired outcome, based on the context. Set‐theoretic methodology promises to enrich and enhance our field, particularly if we use it iteratively with the currently common methodologies, to develop richer and more contextually based theories.

Set‐theoretic methods may well provide an additional set of tools for better understanding leadership in the context in which it occurs.

The authors illustrate accounting information's effects in terms of necessity and sufficiency, using a set-theoretic approach, and highlight how the approach complements conventional correlational analyses.

The authors examine the relationship between accounting numbers (accounting information) and stock prices (effect) under both correlational and set-theoretic perspectives using a value relevance methodology.

The claim that accounting information is significantly correlated to an outcome does not inform the accounting information's necessity or sufficiency. In addition, findings suggest that not all control variables that are significantly correlated to a supposed accounting effect are necessary to explain that effect. Moreover, variables reflecting accounting information are not individually sufficient to explain the effect under investigation.

The study contributes to set-theoretic approach to accounting research and echoes the call for a diversity of research approaches in accounting.

The study may have practical implications for various accounting information users, including investors, financial analysts and financial market and accounting disclosure regulators as well. Indeed, accounting information users should consider the importance of the combined effect of multiple pieces of accounting information in the users' positions on firms' stocks. Understanding what might be the relevant combinations of accounting information associated with a given organizational context is a key in making compelling accounting-informed decisions. Such knowledge can inform reflections of accounting disclosures and regulations on the combined effects of several accounting information.

First, the study adds to the newly introduced set-theoretic approach to empirical accounting. The study also resonates with the call for a diversity of research approaches in accounting. The authors empirically demonstrate that significant correlation between accounting information and its effects does not connote “necessity” or “sufficiency,” which is rather revealed by qualitative comparative analysis (QCA). Such complementarity can help accounting researchers to carry out (1) new investigations of accounting's earlier hypotheses or propositions and (2) investigations of new accounting hypotheses/propositions deriving from existing accounting theories and (3) to explore new relationships between accounting phenomena. Second, the study incidentally contributes to value relevance literature in terms of contextualization of the relevance of accounting information. Specific to the African capital markets, the study complements the few recent studies on the Bourse Régionale des Valeurs Mobilières d’Abidjan (BRVM).

The growing literature on complementarities has drawn attention to how the effects of different organizational structures, practices, and institutions are interdependent. Rather than one best way of organizing, complementarities suggest that the effectiveness of one organizational element may be dependent on the presence or absence of another particular element. Consequently, organizational arrangements often display “multiple equilibria” or what is known as equifinality, whereby multiple pathways may lead to the same or similar outcomes. While being a source of theoretical innovation, the configurational nature of complementarities has posed a number of challenges. This chapter reviews the emerging literature on complementarities to identify a series of conceptual challenges related to understanding complementarities as organizational configurations, and examines the methodological challenges in studying how such elements combine to produce joint effects on performance. The chapter argues that new set-theoretic methods using Qualitative Comparative Analysis (QCA) may present a very useful methodological alternative to studying complementarities. The chapter illustrates this potential by re-analyzing past work by Aoki, Jackson, and Miyajima (2007) on relationships between ownership structure, board structure, and employment practices of listed firms in Japan to show evidence of complementarities associated with hybrid configurations that combine market and relational forms of organization.

This chapter acknowledges the difficulties in the empirical study of social ventures – broadly defined as market-driven ventures that produce social change – that arise from the vast differences among social venture firms in terms of missions, goals, identities, strategies, and structures. In an effort to improve research in this area and advance the field of social entrepreneurship, the authors advocate approaching social ventures from a configurational perspective.

This chapter begins with a discussion of what social ventures are and why organizational configurations – sets of firms that are similar across key characteristics – may be an appropriate perspective to utilize. Then, two methods – cluster analysis and set-theoretic analysis – are discussed in detail as ways to approach the study of social venture configurations. Details include descriptions of the techniques, instructions for use, examples, and limitations for each.

This chapter identifies research opportunities using configurations approaches in social venture research. Substantial possibilities for multilevel and temporally based research are discussed in depth.

A configurational approach can address the incongruence and non-findings in current social venture research and offers new opportunities for future research.

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.

The contrast of multilevel and comparative research may seem counterintuitive at first. After all, one might argue that comparative research on organizations by necessity spans several levels of analysis (Rokkan, 1966). Yet, multilevel and comparative research on organizations present rather distinct traditions in organization studies, each with its own epistemological assumptions and associated methods. Accordingly, an approach that aims to incorporate both multilevel and comparative ideas needs to start with taking inventory of these prior literatures to situate itself. In the following, we thus turn to the literatures on multilevel and comparative research as different traditions with surprisingly little overlap.

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 European Innovation Scoreboard is an important indicator of innovation performance across European Member States. Despite its wide application, the indicator fails to highlight the interlinkages that exist among innovation measures and focuses primarily on the linear relationship between the individual measures and the predicted outcome. This study aims to address this gap by applying a novel technique, the fuzzy-set qualitative comparative analysis (fsQCA), to shed light on these interlinkages and highlight the complexity of the determinants underlying innovation performance.

The authors adopted a configurational approach based on fsQCA that is implemented on innovation performance data from European Member States for the period 2011–2018. The approach is based on non-linearity and allows for the analysis of interlinkages based on equifinality, that is, the model recognises that there are different potential paths of high and low innovation performance. In addition, the approach allows for asymmetric relations, where a low innovation outcome is not the exact inverse of that which leads to high innovation outcome.

The results clearly indicate that innovation outcomes are not based on simple linear relations. Thus, to reap the desired effects from investments in innovation inputs, the complex set of indicators on which innovation performance is based should be taken into consideration. The results clearly indicate the elements of equifinality and asymmetric relations. Different paths lead to high innovation performance and low innovation performance.

The method applied to investigate the determinants of innovation performance is the prime original factor of this study. Thus, the study contributes to literature by highlighting the complexity involved in understanding innovation. By recognising and attempting to detangle this complexity, this study will assist not just academics but also policymakers in designing the necessary measures required to reach this important outcome for a country’s competitive edge.

The notion of configuration – that the whole is best understood from a systemic perspective and should be viewed as a constellation of interconnected elements – is arguably one of the central ideas of organization studies. Yet, this idea also remains one of the field’s least understood aspects. In this volume and its introduction, we outline a new perspective for understanding configuration. Our starting point is the emergence of set theoretic configurational methods, and especially Qualitative Comparative Analysis (QCA), which provides novel ways for analyzing configurations. Our volume goes beyond introducing a new method to the fields of management and organization, as these methods furthermore offer an opportunity to rethink our understanding of the field and to develop different ways of theorizing the rich complexity of relationships that characterize organizational life. In this introduction, we introduce some of the key themes that differentiate the approach taken here from previous work on organizational configurations and provide evidence for the emerging renaissance of the configurational approach in organizational theory and research.