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How Do Researchers (Re-)Use Design Principles: An Inductive Analysis of Cumulative Research

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Accumulating prescriptive design knowledge, such as design principles (DP), is one of the fundamental goals in design science research projects. As previous studies have examined the use of DPs in practice to advance the development and communication of such principles, we argue that this attention also needs to be paid to how and for what researchers (re-)use DPs. Hence, this paper explores DP usage in cumulative (information systems) research based on the analysis and coding of a sample of 114 articles with 226 in-text citations. In doing this, we aim at contributing to the valuable discourse on DP reuse and accumulation by focusing on usage in research, present preliminary types of DP usage extracted from cumulative literature, as well as raise the awareness for guiding user and designer in how to (re-)use and how to allow for reuse of DPs.
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... DPs are abstract rules for the design of artifacts that serve to bridge the gap between research findings and practical issues (Gregor et al., 2020). Researchers have already contributed DPs on different VR domains (e.g., Kohler et al., 2011;Metzger et al., 2017), but existing DPs are often not reused by researchers and practitioners, as design science research (DSR) authors highlight (e.g., Iivari et al., 2021;Schoormann et al., 2021;Strohmann et al., 2023). In particular, designers find it challenging to instantiate DPs in practice (Elshan et al., 2022;Iivari et al., 2021;, and researchers do not build on existing DPs but instead formulate new ones that are often similar in content (Schoormann et al., 2021). ...
... Researchers have already contributed DPs on different VR domains (e.g., Kohler et al., 2011;Metzger et al., 2017), but existing DPs are often not reused by researchers and practitioners, as design science research (DSR) authors highlight (e.g., Iivari et al., 2021;Schoormann et al., 2021;Strohmann et al., 2023). In particular, designers find it challenging to instantiate DPs in practice (Elshan et al., 2022;Iivari et al., 2021;, and researchers do not build on existing DPs but instead formulate new ones that are often similar in content (Schoormann et al., 2021). These issues result in a high density of redundant design knowledge, making it difficult for DP applicants to grasp their prescriptions. ...
... For RQ3, we derive recommendations for future research. This paper contributes theoretically to the consolidation of design knowledge in the VR literature to solve the issue of low DP reusability (Elshan et al., 2022;Iivari et al., 2021;Schoormann et al., 2021). In addition, our paper provides practitioners with an overview of the synthesized status quo to guide them to potentially resolve the issues of low acceptance of VR applications (especially for collaborative work) (Menck et al., 2023;Schöbel & Tingelhoff, 2023). ...
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Virtual reality (VR) is a promising technology offering immersive environments for diverse applications, including collaborative work. However, VR applications often lack user acceptance due to usability and cognitive load issues. Prescriptive design knowledge and appropriate design principles (DPs) can address these issues, but their low reusability in research and practice limits their effectiveness. This paper aims to enhance DP reusability through a meta-study of VR design knowledge, with a special focus on collaborative work. We examine 65 DPs through the lens of design science research criteria, synthesize them into a reusable set of 11 DPs via AI-based prompt engineering, and delineate future research recommendations. This paper contributes to research by consolidating existing design insights, which helps to avoid redundant design knowledge proposals. It contributes to practice by providing a reusable overview of DPs for VR applications in collaborative settings, thereby aiding in leveraging VR's full potential.
... DPs, as the most common contribution, represent abstract rules as a generalization for a class of problems [3,4]. Scholars increasingly publish DPs for different contexts [5], but DPs are seldom reused by researchers and practitioners [6,7]. Our paper addresses the low DP reusability, which is reflected in several issues. ...
... Our paper addresses the low DP reusability, which is reflected in several issues. First, DSR authors often start from scratch to develop DPs for specific topics instead of building on existing ones [6]. Therefore, DPs do not reach the stage where they are reused multiple times and recognized as established theory [8]. ...
... The authors stated that many DPs are not easily understandable and rarely bridge the gap between conceptual formulation and practice [7]. While these works mainly address practical reusability, Schoormann et al. [6] highlighted DPs' reusability in research [6]. Through a review of 114 DP articles with 226 in-text citations, they discovered that DPs are rarely reused by further scholars. ...
... Generative AI has the potential to extract such design knowledge that is spread over a broad body of interdisciplinary research and make it available in a collective form for scholars and practitioners. This could also overcome the limitation that design knowledge is currently rarely reused, which hampers the fundamental idea of knowledge accumulation in design science research (Schoormann et al. 2021). ...
... At this point, generative AI has the potential to extract such design knowledge that is spread over a broad body of interdisciplinary research and make it available in a collective form for scholars and practitioners. This could also overcome the limitation that design knowledge is currently rarely reused, which hampers the fundamental idea of knowledge accumulation in design science research (Schoormann et al., 2021). ...
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Published version: Feuerriegel, S., Hartmann, J., Janiesch, C. et al. Generative AI. Bus Inf Syst Eng (2023). https://doi.org/10.1007/s12599-023-00834-7
... However, researchers observed that DSR does not follow a systematic knowledge accumulation strategy in terms of refining or extending previous design knowledge [30,33]. A recent JAIS Special Issue Editorial on accumulation and evolution of design knowledge in DSR states that typical DSR contributions are rather isolated and the provided design knowledge has a monolithic structure [6]. ...
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Design science research (DSR) intends to contribute design knowledge to the scientific discourse in the field of Information Systems. However, such design knowledge is typically produced in isolated DSR projects and does not follow a systematic knowledge accumulation strategy in terms of refining or extending previous design knowledge. Towards a first step in fostering design knowledge reuse and supporting knowledge accumulation in DSR, we propose a design knowledge search engine facilitating access to existing design knowledge. In this paper, we present a knowledge extraction template, a mechanism to automatically extract design knowledge of existing DSR publications based on this template as well as a search interface. Applying DSR ourselves, we report on the preliminary results and findings of the first design cycle and deliver (1) design requirements derived from the literature, (2) propose an initial set of design principles, (3) a prototypical implementation called DEKNOWSE, and (4) an evaluation of the first design cycle in a focus group with DSR researchers. We contribute design knowledge for the class of design knowledge extraction and search systems.
... We draw on extant research findings to codify legal design knowledge. In particular, we use research regarding the visualisation and codification of design knowledge (Chandra Kruse & Nickerson, 2018;Schoormann et al., 2021;Vom Brocke et al., 2020) but in particular also literature on the specifics of legal knowledge Knackstedt et al., 2014;Vaujany et al., 2018), visual inquiry tools (Osterwalder & Pigneur, 2013), and cognitive load theory (CLT) as our kernel theory to develop our design pattern framework. CLT provides a theoretical framework for how individuals process information during learning and problem-solving processes while guiding the structuring of information for better learning results (Sweller, 1988). ...
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New political objectives, emerging regulatory regimes for the digital sphere, and higher penalties for violations have intensified the pressure to develop lawful IT artefacts. As the adaptation of existing IT artefacts to new regulations can be expensive and arduous, a more attractive approach would be to design IT artefacts lawfully from the beginning. A major challenge is that the law is generally technology-neutral, and lawful design requires legal expertise throughout the development, which is costly and time consuming due to communication challenges between legal experts and developers. One possible approach to proactively consider IT regulations in the systems development is design patterns that convey legal design knowledge and support developers in determining the appropriate design options. Consequently, we develop a framework for lawful design patterns and demonstrate their feasibility and advantages using the example of developing AI-based assistants and the regulation of the General Data Protection Regulation (GDPR). Using the design pattern framework, we develop design patterns for lawful AI-based assistants and evaluate them using (a) an experimental approach to show the usefulness of the patterns for developers and (b) rely on a legal simulation study to holistically evaluate how design patterns contribute to lawful IT.
... They are the outcome of design-oriented studies that do not seek to explain phenomena but develop prescriptive knowledge about classes of sociotechnical systems (Baskerville, Baiyere, Gregor, Hevner, & Rossi, 2018;Chandra, Seidel, & Gregor, 2015;Hevner, March, Park, & Ram, 2004;Schoormann, Möller, & Hansen, 2021). The focus is thus on intervention (van den Akker, 1999). ...
... A review is presented by [43] that defines "design principle", distinguishes between prescriptive and descriptive types, and reviews methods for their extraction and evaluation. A related literature review focused on criteria for the re-use of design principles [44]. ...
Chapter
Design science research (DSR) intends to contribute design knowledge to the scientific discourse in the field of Information Systems. However, such design knowledge is typically produced in isolated DSR projects and does not follow a systematic knowledge accumulation strategy in terms of refining or extending previous design knowledge. Towards a first step in fostering design knowledge reuse and supporting knowledge accumulation in DSR, we propose a design knowledge search engine facilitating access to existing design knowledge. In this paper, we present a knowledge extraction template, a mechanism to automatically extract design knowledge of existing DSR publications based on this template as well as a search interface. Applying DSR ourselves we report on the preliminary results and findings of the first design cycle and deliver (1) design requirements derived from the literature, (2) propose an initial set of design principles, (3) a prototypical implementation called DEKNOWSE, and (4) an evaluation of the first design cycle in a focus group with DSR researchers. We contribute design knowledge for the class of design knowledge extraction and search systems.KeywordsDesign Science ResearchKnowledge AccumulationDesign Knowledge Search
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Many Design Science Research (DSR) papers in Information Systems (IS) suggest sets of design principles (DPs) that provide knowledge for creating instances, in different contexts, of IT artifacts that belong to the same class. However , despite frameworks for evaluating DSR contributions, the evaluation of DP reusability to, with and for practitioners has been largely neglected. We suggest that in order to maintain the practical relevance of DSR, papers with DPs as their key outcomes should contain a reusability evaluation of the proposed principles. We propose a framework of minimum reusability evaluation of DPs by members of the target community of practitioners. The framework comprises five criteria: (1) accessibility, (2) importance, (3) novelty and insightfulness, (4) actability and guidance, and (5) effectiveness.
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