Studying digital footprints demands novel methodological approaches to draw meaningful insights and derive actionable conclusions. With this orientation in mind, I have deliberately chosen qualitative comparative analysis (QCA) and computer-aided text analysis (CATA) as the cornerstone methodologies for my research.

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QCA, deeply rooted in set-theory, enables researchers to systematically identify configurations of conditions leading to particular outcomes (Ragin, 2000, 2008b, 2008a). Its core strength lies in its capacity to reveal causal complexity (Furnari et al., 2021; Misangyi et al., 2017) and the potential for equifinality, where multiple and distinct pathways can lead to the same outcome (Oana et al., 2021; Schneider & Wagemann, 2012). In essence, QCA focuses on understanding how different elements or conditions combine, rather than evaluating them in isolation, to provide nuanced explanations of the observed phenomenon (Fiss, 2011).

Conversely, CATA brings to the forefront the prowess of computational techniques, encompassing a variety of methods like the dictionary-based approach (McKenny et al., 2018; Moss et al., 2014; Payne et al., 2011), rule-based approach (Hutto & Gilbert, 2014), topic modeling (Hannigan et al., 2019), and machine learning (Leavitt et al., 2021; Williamson et al., 2020). This analytic strategy is adept at handling vast textual data, extracting key themes, sentiments, or patterns, and allowing researchers to draw inferences with unprecedented granularity and scale (Humphreys & Wang, 2018).

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Recognizing the strengths of both QCA and CATA, and in a bid to bridge the qualitative-quantitative divide, I introduced in my dissertation work a novel mixed-method approach - Qualitative Text Comparative Analysis (QTCA). This approach goes beyond simply placing techniques next to each other or using them in parallel; instead, it facilitates the fusion and integration of these techniques (Meurer & Waldkirch, 2022). Integrated mixed methods designs offer the potential for more robust meta-inferences. By leveraging the strengths of one method to offset the limitations of another, or by enhancing the theoretical coherence between individual components, these designs improve the overall efficacy and integration of various elements within a study (Meuer & Rupietta, 2017).