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Sample size in qualitative research has been the subject of enduring discussions [4, 10, 11]. Whilst the quantitative research community has established relatively straightforward statistics-based rules to set sample sizes precisely, the intricacies of qualitative sample size determination and assessment arise from the methodological, theoretical, epistemological, and ideological pluralism that characterises qualitative inquiry (for a discussion focused on the discipline of psychology see [12]). This mitigates against clear-cut guidelines, invariably applied. Despite these challenges, various conceptual developments have sought to address this issue, with guidance and principles [4, 10, 11, 13,14,15,16,17,18,19,20], and more recently, an evidence-based approach to sample size determination seeks to ground the discussion empirically [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35].
Pragmatic approaches to qualitative analysis are likely valuable for IS researchers yet have not received enough attention in the IS literature to support researchers in using them confidently. By pragmatic approaches, we mean strategic combining and borrowing from established qualitative approaches to meet the needs of a given IS study, often with guidance from an IS framework and with clear research and practice change goals. Pragmatic approaches are not new, but they receive less attention in qualitative research overall and are not always clearly explicated in the literature [9]. Part of the challenge in using pragmatic approaches is the lack of guidance on how to mix and match components of established approaches in a coherent, credible manner.
Framework analysis comes from the policy sphere and tends to have a practical orientation; this applied nature typically includes a more structured and deductive approach. The history, philosophical assumptions, and core processes are richly described by Ritchie and Spencer [36]. Framework analysis entails several features common to many qualitative analytic approaches, including defining concepts, creating typologies, and identifying patterns and relationships, but does so in a more predefined and structured way [37, 38]. For example, the research team can create codes based on a framework selected in advance and can also include open-ended inquiry to capture additional insights. This analytic approach is well-suited to multi-disciplinary teams whose members have varying levels of experience with qualitative research [37]. It may require fewer staff resources and less time than some other approaches.
The framework analysis process includes five key steps. Step 1 is familiarization: Team members immerse themselves in the data, e.g., reading, taking notes, and listening to audio. Step 2 is identifying a coding framework: The research team develops a coding scheme, typically using an iterative process primarily driven by deductive coding (e.g., based on the IS framework). Step 3 is indexing: The team applies the coding structure to the entire data set. Step 4 is charting: The team rearranges the coded data and compares patterns between and within cases. Step 5 is mapping and interpretation: The team looks at the range and nature of relationships across and between codes [36, 39, 40]. The team can use tables and diagrams to systematically synthesize and display the data based on predetermined concepts, frameworks, or areas of interest. While more structured than other approaches, framework analysis still presents a flexible design that combines well with other analytic approaches to achieve study objectives [37]. The case example given in section 3 offers a detailed application of a modified framework analytic approach.
Building on the discussion of pragmatic combination of approaches for a given study, we turn now to the question of ensuring and

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