The chapter re-examines the case study research method and its role and contribution to the IS discipline and focuses on the current status of the case study research and the increased digitalization. The advantages of qualitative interpretive cases studies are identified, recent case studies are described and analyzed, and their contributions highlighted. These examples continue to enhance the discipline and sustain the traditional benefits of the case study research through rich data, analysis and understanding the links between people, organizations and technologies, the advancement and expansion of theory, the identification of hidden aspects, and the emergence of new concepts and theorization. Two of the cases use trace data, a type of data emerging as a product of digitalization. While these cases provide contributions, they also challenge the traditional understanding of what a case study is, and the benefits that accrue. The chapter emphasizes the need for mixed-method and multi-method case studies research in addition to trace data to enhance the benefits of the case study research.

This chapter introduces Bayesian reasoning for qualitative research, highlights contributions to methodological literature, and overviews the book.

This chapter illustrates how to apply explicit Bayesian analysis to scrutinize qualitative research, pinpoint sources of disagreement on inferences, and facilitate consensus-building discussions among scholars, highlighting examples of intuitive Bayesian reasoning as well as departures from Bayesian principles in published research.

This chapter presents the case for employing Bayesianism as a universal, unified framework for inference that narrows the divide between qualitative versus quantitative data, within-case versus cross-case analysis, and observational versus experimental research. It offers a Bayesian critique of various other approaches to qualitative methods and multi-method research.

It significantly strengthens the inferences drawn based on QCA results if we connect these results to theoretical knowledge and within-case evidence before, during, and after the analysis. In this chapter, we discuss two prominent tools of doing so after the analytic moment – set-theoretic theory evaluation and set-theoretic multi-method research (SMMR) – and demonstrate their implementation within R. Theory evaluation is a form of re-assessing theoretical hunches based on the results generated by QCA. While it can also be used for the identification of interesting cases for follow-up case studies, this task is better achieved with set-theoretic multi-method research. The latter is a tool for identifying typical and deviant cases for comparative or single within-case analysis.

Learning goals:

- Basic understanding of what theory-evaluation and set-theoretic multimethod research are.

- Familiarity with how to apply formal set-theoretic theory evaluation for re-assessing theoretical hunches based on the results generated by QCA.

- Familiarity with how to use set-theoretic multi-method research (SMMR) for the identification of cases for follow-up case studies after QCA.

- Ability to implement theory evaluation and SMMR in R.

If case study comparison is useful in the social sciences, it should be at least as useful as a way of understanding the political preferences and participation of individuals as it is for larger and more complex social categories such as organizations, parties, militaries, or states. Thus, qualitative comparisons involving the political preferences and activity of American billionaires will serve as a benchmark for the plausibility of various comparative frameworks. The chapter will demonstrate that applying Mill’s method-type comparison to billionaires generates inferential absurdities and also shows that regression-type logics of control are a poor fit for individual-level qualitative analysis. More feasible frameworks for understanding qualitative comparison at the individual level come both earlier and later in the inferential process. Cross-case comparisons are highly valuable for concept formation and theory building prior to any systematic effort at causal inference and also play a meaningful role in inferences about moderation effects after process tracing has been successful within each case. Furthermore, multi-case qualitative analysis that is not structured as explicit comparison can make crucial contributions to multi-method research on individuals.