Design Science Applications in Information Systems
Summary
Design Science Research (DSR) in Information Systems (IS) seeks to advance both theoretical understanding and practical functionality by constructing and evaluating purposive artefacts. These artefacts may take the form of models, methods, constructs or instantiations which address complex organisational challenges such as data interoperability, decision support and stakeholder collaboration. Central to this approach is a rigorous process that alternates between problem diagnosis, solution design and iterative evaluation. In recent years, DSR has embraced interdisciplinary insights—drawing on data science, human–computer interaction and systems engineering—to refine its methodological rigour and enhance real-world impact. Key advances include the articulation of evaluation frameworks that balance formative feedback with summative assessment, as well as the codification of design principles to guide artefact development at both project and ecosystem levels. The global significance of DSR in IS is evident in its contributions to digital marketplaces, intelligent public-sector tools and standardised evaluation strategies, all of which exemplify the translation of scholarly knowledge into robust, context-sensitive solutions.
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Design Science Applications in Information Systems publication trend
The graph below shows the total number of articles in design science applications in information systems across all publications each year (not limited to Nature Index journals).
Technical terms
Design Science Research (DSR): A research paradigm focused on creating and evaluating purposeful artefacts to solve identified organisational problems.
Artefact: A constructed object—such as a model, method or software prototype—designed to address a specific challenge within an IS context.
Formative evaluation: Assessment activities conducted during artefact development to provide ongoing feedback and support iterative refinement.
Summative evaluation: Conclusive assessment undertaken after artefact completion to determine effectiveness against stated objectives.
Data harmonisation: The process of aligning disparate data sources to achieve consistency, accuracy and interoperability.
References
- Improving efficiency and quality of operational industrial production assets information management in customer–vendor interaction. Journal of Industrial Information Integration (2024).
- Data-driven intelligence in crisis: The case of Ukrainian refugee management. Government Information Quarterly (2025).
- FEDS: a Framework for Evaluation in Design Science Research. European Journal of Information Systems (2016).
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