Abstract

Design Science Research (DSR) has emerged as a robust methodology for addressing complex real-world problems through the creation and evaluation of innovative technical artifacts (Hevner et al., 2004; Peffers et al., 2007). Computational DSR has become an increasingly prevalent paradigm in Information Systems (IS) research due to the exponential growth in data availability, computational resources, and advancements in algorithms to solve contemporary challenges that are challenging to solve using traditional methods. These technological advancements have also spearheaded the Computationally Intensive Theory Creation (CITC) framework (Berente et al., 2019; Miranda et al., 2022) which outlines how computational methods and data sources can be used to create new theoretical insights. DSR is characterized by multifaceted interactions that drive the iterative development of artefacts. While existing theory can be used to frame and contextualize these dynamic interactions, the design process may also reveal patterns that lead to the creation of novel theory. These theories can come from multiple disciplines as DSR is multidisciplinary in nature; likewise, DSR capitalizes on this pluralism to draw methodological approaches from multiple fields. CITC complements DSR’s iterative and multidisciplinary nature as CITC’s core principles are centered around reflexivity, methodological pluralism, and epistemic attention to all research operations. CITC can complement DSR through its core principles; reflexivity is aligned with iterative improvement during the research process, methodological pluralism in CITC supports DSR’s multidisciplinary nature, and epistemic attention ensures rigorous scrutiny of each stage in the DSR process, enriching the validity and robustness of DSR-generated artefacts. In addition, CITC’s lexical framing and pattern surfacing are techniques can improve the DSR process and enhance theory building by providing a framework to follow. CITC’s stopping rules can ensure methodological rigor, computational efficiency, and theoretical robustness of theoretical artefacts created in the DSR process (Miranda et al., 2022).

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