This chapter provides an overview of the theory of relationality – the idea that people care about how others relate to them, and whether they can successfully relate to others – and how potential collaborators can be uncertain about these relational aspects. “Relating to others” captures both the information to be shared, and also the experience of interacting. Key to the theory of relationality, as it applies to potential collaborators with diverse forms of expertise, is that status-based stereotypes can drive a wedge between having expertise and having that expertise be socially recognized. This chapter builds up to a series of hypotheses about how potential collaborators care about the information to be shared and the experience of interacting when choosing whether to engage in new collaborative relationships with diverse thinkers. It also identifies several possible interventions for fostering valuable new collaborative relationships.

This chapter tests two ways of overcoming uncertainty about relationality – having potential collaborators directly communicate how they will relate to each other, and using third parties such as matchmakers and boundary spanners. Both are useful for creating valuable new collaborative relationships, especially between people who begin as strangers. In addition, this chapter also presents evidence showing the impact of new collaborative relationships on strategic decision-making. Data in this chapter come from a variety of national surveys, field experiments, and case comparisons.

Most applied QCA, and thus applied SMMR, focus on claims of sufficiency. Some, though, also includes claims of necessity. In this chapter, it is explained how the SMMR principles and practices developed for claims of sufficiency also work for claims of necessity. It starts with the simplest possible, and also most often encountered, form of necessity claims: that of a single condition being necessary for the outcome. After this, disjunctive and then conjunctive necessity claims are discussed. Learning goals: - Understand that only minor adjustments are needed to SMMR types of cases, forms of single-case and comparative designs, principles, formulas, and ranks when the cross-case solution postulates a necessary condition - Consolidate the knowledge of SMMR principles, types of cases, formulas, and ranks - Further practice the use of the smmr() function and the interpretation of its output

This concluding chapter discusses various aspects related to the use of SMMR: how does SMMR relate to existing advice for case selection; in which sequence should the different SMMR designs be applied; how should one choose among SMMR designs if all of them cannot be performed due to constraints (time, money, data, etc.); which types of sets (crisp or fuzzy) should one use when planning to perform SMMR; how can SMMR be fruitfully combined with theory evaluation, robustness tests, cluster diagnostics, and procedures that integrate time and temporality into the QCA? Learning goals: - Reflect on challenges in putting SMMR into practice - Understand the different ways in which to choose among the various SMMR designs - Learn about the implications of using different types of sets in SMMR - Develop ideas on how to combine SMMR with other, advanced tools in set-theoretic methods that share the feature of classifying cases - Further appreciate the full suit of SMMR designs as the yardstick for measures to be taken to strengthen descriptive and causal inference

The full level of complexity consists of the joint presence of disjunctions and conjunctions. This chapter illustrates how all SMMR principles, types of cases, ranks, and formulas introduced in the previous chapters suffice to guide case selection for within-case analysis. Using empirical examples, it illustrates the various relations of necessity and sufficiency that can occur between the cross-case condition and outcome, on the one hand, and the mechanism at the within-case level, on the other. The chapter also explains how and why all QCA solution types – conservative, most parsimonious, intermediate – can serve as the basis for causal and descriptive inference in SMMR. Learning goals: - Practice all SMMR designs on a typical QCA solution formula showing full complexity (disjuncts and conjuncts) - Get acquainted with the conclusions drawn from evidence on a case’s membership in the within-case mechanisms - Understand that all QCA solution types – conservative, intermediate, most parsimonious – can be the basis for descriptive and causal inference SMMR designs

This introductory chapter situates SMMR in the broader context of the literature on multi-method research. It outlines the key basic elements of SMMR: the notion of cross-case and within-case level analyses; the descriptive and causal inference analytic goals of SMMR; the main types of cases (typical, deviant consistency, deviant coverage, and individually irrelevant (iir)); single-case and comparative SMMR designs; types of sets; types of QCA solution formulas; and a flowchart of the process of performing SMMR. In the section on useful practical information, the chapter details the prerequisites for successfully mastering the material contained in the book; the use of example data and of resources contained in the book; key SMMR terminology; and the structure of the book, which follows the logic of starting with simple examples and then increasing complexity chapter by chapter.

The second element of causal complexity consists in the presence of conjunctions. In this chapter, the analytic consequences for SMMR are detailed and solutions for containing these consequences are formulated. Those strategies consist in applying further SMMR principles and in selecting cases based on whether their selection adheres to those principles. Learning goals: - Understand the challenges for causal inference SMMR designs triggered by conjunctions - Learn about how additional principles guide case selection in causal inference SMMR designs in the presence of conjunctions - Distinguish between focal and complementary conjuncts - Get acquainted with ranks for cases and case pairs in causal inference SMMR designs and how those ranks reflect which SMMR principles are fulfilled and which ones are violated - Learn about INUS conditions that qualify as necessary for the outcome and the consequences this triggers for purposeful case selection in causal inference SMMR designs - Understand the reasons why increased complexity of QCA solution formulas in the form of conjunctions also increases the complexity of causal inference SMMR designs

This chapter focuses on one element of causal complexity: disjunctions, or equifinality. Using an example from applied research, it is explained which new SMMR principles and subtypes of cases need to be formulated to capture the consequences for descriptive and causal inference triggered by disjunctions. Learning goals: - Understand the inferential challenges triggered by disjunctions (equifinality) - Get acquainted with the additional sub-types of cases produced by disjunctive solution formulas - Understand how additional principles guide case selection in the presence of disjunctions - Learn if and how moving up the ladder of generality can be used to theorize away disjunctions and the inferential challenges it poses - Become more familiar with the smmr() function and the interpretation of its output in the presence of disjunctions