Advances in Design Science Research
Permanent URI for this collection
Browse
Recent Submissions
Item With Great Power Comes Great Responsibility: Responsible Management of Artificial Intelligence in Supporting Design Research Activities(2025-01-07) Schoormann, Thorsten; Gupta , Samrat; Möller, Frederik; Chandra Kruse, LeonaNovel technologies are getting growing attention in our scholarly lives and are shifting the way traditional research practices are used. This paper explores how design research is affected by technological advancements and what potential risks can arise from that. By surveying information systems (IS) design literature, we observed a focus on enhancing the design outcome through computational, data-driven, and AI-driven approaches. Only a few papers in our sample provide insights into how the design process, from problem understanding through artifact building to evaluation, can be supported. We therefore explore the opportunities of delegating tasks between human designers and technology by presenting examples from the broader design research literature and borrowing illustrations from adjacent fields. Our work aims to open up the solution space of technology-aided design processes and creates awareness of the responsible handling of technology in design research.Item Design Science Research Labs: A Framework and Design Principles(2025-01-07) Dreyer, Susanne; Mcteague, Chris; Thoring, KatjaResearch labs are common in many disciplines but are rarely addressed in Design Science Research (DSR). This paper explores the value and potential of DSR labs and the importance of the research environment in Information Systems (IS) by learning from other DSR-related fields. A multi-case study of 12 DSR-related research labs across Europe was conducted. The study presents common lab setups, a framework, and design principles for future DSR labs, focusing on the lab environment, research methods, and technology-driven and innovative approaches for conducting DSR. It emphasizes the importance of research space as an influential factor in DSR and DSR education. Moreover, it offers strategies and guidance for designing research lab structures that support various phases of the DSR process. The findings are relevant for researchers and practitioners who want to set up a transdisciplinary and technology-driven DSR lab.Item Addressing Challenges in a Dangerous World: Developing a Design Science Artifact for Advancing Open Source Intelligence (OSINT) Research(2025-01-07) Kayser, Franz; Mayer, Thomas; Bücker, MichaelOpen Source Intelligence (OSINT), deriving intelligence from public data, has gained scrutiny since the Russian invasion of Ukraine. Despite numerous attempts at standard definitions, research around technology-driven intelligence gathering and analysis remains ambiguous. This paper uses a Design Science Research (DSR) approach to categorize the technology-driven intelligence construct. Analyzing sixty studies via structured literature review, three domains were identified: maturity, Intelligence Cycle phase, and use case. The resulting framework, developed into a trend radar, was evaluated with expert interviews, revealing technological gaps in planning/direction and dissemination/integration phases. While intelligent support technologies were noted, practical implementation lags behind theory. The human factor remains central to OSINT. Findings suggest future research should develop applications for underserved phases and examine why proven applications are not widely adopted, considering legal, ethical, political, and social factors. This study contributes to technology-driven intelligence literature as a knowledge base, research gap identifier, and guide for further research.Item A Hermeneutics-Inspired Learner Self-Reflection Tool for DSR Courses: The Case of ADR and eDSR(2025-01-07) Sammon, David; Tuunanen, Tuure; Nguyen, Andy; Nagle, TadhgThis paper tracks the evolution (across three iterations) of a hermeneutics-inspired learner self-reflection tool for DSR courses. Specifically, this Learning Analytics (LA) model is designed, instantiated, and evaluated for research methods courses, one on Action Design Research (ADR), delivered in an Irish University in 2023, and one on echeloned DSR (eDSR), delivered in a Finnish University in 2024. Iteration One sees the emergence of a hermeneutics-inspired design principle for a “Learning-by-Doing” pedagogical approach; Iteration Two sees the emergence of our first attempt at the datafication of learner “interim struggles” as part of the learning process (using v1 of a learner self-reflection tool); Iteration Three sees the emergence of v2 of the learner self-reflection tool and its instantiation in a culturally and topically diverse learning environment (to Iteration Two). Our iterative approach refines the LA model to better align learner self-reflections with the evolving complexities of teaching DSR (ADR and eDSR).Item Towards Design Patterns for Information Systems – Finding Suitable Artefact Type Candidates for a Design Problem(2025-01-07) Lösser, Benedict; Winter, RobertThe artefact is at the core of Design Science Research in Information Systems (DSR). The growing variety and complexity of designs makes selecting the best-suited artefact type in a research project more challenging. Extant classification approaches do not provide much support for identifying the most promising solution paths, as they focus on artefact properties and less on problem characteristics. As already illustrated in related disciplines like engineering science, differentiation of a functional (problem-oriented) and a construction (solution-oriented) perspective promises to be useful for supporting the design process. Our study intends to develop and utilize this differentiation for DSR projects. Based on associations between problem and solution classes, design patterns could help researchers to identify the most promising artefact type candidates, and thus avoid less promising con-struction paths. First, we develop a classification scheme for the functional design perspective. Second, we modify existing DSR artefact classifications to develop a scheme for the construction design perspective. Third, based on a polar sample, we identify initial functional clusters and develop preliminary design patterns as associations between functional clusters and construction types. Our contribution constitutes a first, exploratory step towards more effective design guidance in the early DSR stages.Item Synthetic Construct Validity: A Transformational Solution(2025-01-07) Sang, Lan; Larsen, Kai; Gandhi, FairyIn the information systems (IS) discipline, ensuring the accuracy of psychometric measurement through construct validation is paramount for theoretical advancement. Construct validity delineates the degree to which an instrument genuinely measures the concept it purports to assess. Traditional methods like principal components analysis can bias results by employing the same dataset to validate the instrument and test the proposed theory. In contrast, our research introduces a novel computational tool for evaluating construct validity, which validates the instrument separately from the data collected to test the theory. By finetuning a RoBERTa-based transformer model, we estimate the likelihood of indicator pairs being associated with the same construct. These probabilities serve as measures of indicator-to-construct concordance. Our empirical findings reveal significant congruence between our method's probabilistic evaluations and established factor loading measures. The proposed approach improves construct validity assessments and refines IS measurement tools, paving the way for more precise and reliable theoretical model development.Item Introduction to the Minitrack on Advances in Design Science Research(2025-01-07) Baskerville, Richard; Tuunanen, Tuure; Rossi, Matti