VOLUME I. Crisp-Set Qualitative Comparative Analysis (csQCA) and Its Relation to Innovation: Discussion and Interpretation of Results

The objective of this work is to introduce the reader to the basic concepts of using Qualitative Comparative Analysis (QCA) with crisp-set data (csQCA) through demonstrations of its use with various examples in the administration of innovation. Since the late 1980s, its precursor, Qualitative Comparative Analysis (QCA), has been at the forefront of social science methodology because it is based on the set of established relationships and objectives for discovering sufficient and necessary conditions.

It should be noted that it is common to refer to the original Boolean version of QCA as csQCA, where cs (crisp-sets) denotes a set of crisp data. The version that allows for the use of multi-category conditions is called mvQCA, where mv (multi-value) describes the multiple values. The fsQCA version, where fs (fuzzy-set) describes the set of fuzzy data. The goal of QCA analysis, in general, is to account for a particular outcome, as opposed to regression-based analyses, which typically aim to be foundational tools for explaining the effects of causes (Wagemann and Schneider, 2010).

Regarding the state of social sciences as a general foundation for innovation management, Sartori (1970) stated:

"...one observes a poor state of science oscillating between two weak extremes: unconscious thinking, which is the overwhelming majority, and overly conscious thinking, done by a small minority..."

Calling on scholars to acquire training in basic logic:

"To steer a middle course between crude logical mismanagement on one side and logical perfectionism (and paralysis) on the other..."

It was in the late 1980s when Dr. Charles Ragin introduced Boolean algebra and set theory to the social sciences with his groundbreaking book The Comparative Method (Ragin, 1987), which describes in depth all the components of QCA. Nevertheless, the real stimulus in attention began some years later with the book Fuzzy-sets Social Science (Ragin, 2000). By now, there are scholars using configurational comparative methods, given the possibility of formalizing case-oriented analysis and thus offering tools to improve comparative research. These methods are particularly suited for identifying the minimally necessary and/or minimally sufficient (combinations of) conditions that produce an outcome of interest (i.e., assessing the causes of effects), with great potential for quantitative applications such as in engineering (Mendel and Korjani, 2010; Marks, et al., 2018).

Given the advantages offered, recent discussions on configurational comparative methods suggest that quantitative approaches based on regressions and QCA are best applied together (Ragin 2008; Schneider and Wagemann 2010; Rihoux 2006). However, there is a warning for enthusiasts that academics should not become monomaniacs of QCA (Ragin and Rihoux, 2004, p.6).

On the other hand, there are early efforts of fsQCA application in the field of entrepreneurship and innovation, such as the work of Kraus et al. (2017), where a compilation of 77 articles published from 2005 to 2016 with keywords: fsQCA, business management, entrepreneurship, and innovation, reveals a gradual increase in these fields for the use of fsQCA. Thus, innovation-oriented management sciences can leverage what has been achieved in social sciences through the significant contributions of Dr. Ragin. Therefore, this work consists of eleven chapters, which we briefly describe:

CHAPTER 1. Regression Analysis vs. Qualitative Comparative Analysis (QCA). This chapter presents how regression works and its use by social researchers oriented towards innovation management; the most common errors made, and therefore, the inconveniences for research. This potentially results in errors when assuming hypotheses that, when subjected to QCA, lose much of their effectiveness. The chapter concludes with the description of fsQCA, which allows for visualizing its foray into innovation management sciences.

CHAPTER 2. QCA Software and fsQCA Configuration. This chapter enables the reader to understand the scope of the fsQCA software, designed by Dr. Charles Ragin, to learn about each of its functions, from creating and opening a file in different formats to configuring and testing the available functions, in twelve practical exercises using the fsQCA software described in detail.

CHAPTER 3. Qualitative Comparative Analysis of Crisp-set Data (csQCA). This chapter introduces the reader to a very common type of data used in csQCA, revealing basic concepts through the use of binary data, Boolean negation, the use of truth tables, groupings, Boolean addition and multiplication, combinatorial logic, main implications, De Morgan's Law, necessary and sufficient causes, and minimization. It addresses concepts about counterfactual cases, the problem of coincident cases, how counterfactual cases compare vs. QCA, and a contrast with conventional quantitative research. Six exercises using fsQCA software confirm the use of the main concepts.

CHAPTER 4. Qualitative Comparative Analysis of Fuzzy-set Data (fsQCA). This chapter introduces the reader to a very special type of data, the fuzzy type used by fsQCA software, revealing basic concepts through understanding its nature, the possibility of using them in levels and continuously. Fuzzy-set data operations, such as Boolean negation, conjunction (logical AND), union (logical OR), are presented. It includes models and calibration methods, analysis of necessary and sufficient conditions, consistency, how to create truth tables, and analyze the corners of vector space. Fourteen exercises demonstrate the use of fsQCA software, confirming the use of the main concepts.

CHAPTER 5. Evaluation of fsQCA Data Set. The chapter describes the importance of what is known as INUS and SUIN conditions, as well as consistency and coverage in general. The manual development of five cases confirms the use of the main concepts.

CHAPTER 6. Calibration and Its Importance in fsQCA. This chapter opens a debate on the implications of calibration before starting measurements in scope and context, the pros and cons of using indicators in quantitative research, SEM vs. fsQCA relationship, qualitative research, and the need for calibration, direct and indirect calibration methods. The manual development of four cases confirms the use of the main concepts.

CHAPTER 7. Configurational Thinking in fsQCA. This chapter demonstrates the importance of configurational thinking from a qualitative and quantitative perspective, discussing the evaluation of membership degree in a configuration and the comparison of causal paths.

CHAPTER 8. Net Effects in fsQCA. The chapter describes what is meant by net effects, the associated problems, proposes a shift in focus to types of cases, exposes the comparison of fuzzy data cases, and the analysis of configurations.

CHAPTER 9. Net Effects vs. Configurations in fsQCA. Through two hypothetical cases, the study is conducted, and each of the concepts discussed is developed, explaining their results by comparison, to understand the nature of net effects versus configuration by fsQCA.

CHAPTER 10. csQCA Analysis Guide. With crisp data, from an innovation case, a quick and visual guide is offered to the reader, through screenshots and eight exercises on how to solve a case through the analysis of necessary conditions, representing data in the truth table, identifying contradictory and logical remainders, sufficiency analysis, truth table minimization for occurrence and non-occurrence of the phenomenon, and result interpretation.

CHAPTER 11. fsQCA Analysis Guide. With fuzzy data, from an innovation case, a quick and visual guide is offered to the reader, through screenshots and nine exercises on how to solve a case through accessing the work file, analyzing necessary conditions with the occurrence of the phenomenon and its reports, interpreting coverage, generating the truth table, applying the Standard Analyses option, interpreting generated solutions, generating the truth table with non-occurrence of the phenomenon, analyzing main implications, and case interpretation.

GLOSSARY. A glossary of the most commonly used terms is provided.