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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Design science (methodology) | 2/2 | https://en.wikipedia.org/wiki/Design_science_(methodology) | reference | science, encyclopedia | 2026-05-05T10:02:14.418805+00:00 | kb-cron |
Artifacts within DSR are perceived to be knowledge containing. This knowledge ranges from the design logic, construction methods and tool to assumptions about the context in which the artifact is intended to function (Gregor, 2002). The creation and evaluation of artifacts thus forms an important part in the DSR process which was described by Hevner et al., (2004) and supported by March and Storey (2008) as revolving around “build and evaluate”. DSR artifacts can broadly include: models, methods, constructs, instantiations and design theories (March & Smith, 1995; Gregor 2002; March & Storey, 2008, Gregor and Hevner 2013), social innovations, new or previously unknown properties of technical/social/informational resources (March, Storey, 2008), new explanatory theories, new design and developments models and implementation processes or methods (Ellis & Levy 2010).
== A three-cycle view == DSR can be seen as an embodiment of three closely related cycles of activities. The relevance cycle initiates DSR with an application context that not only provides the requirements for the research as inputs but also defines acceptance criteria for the ultimate evaluation of the research results. The rigor cycle provides past knowledge to the research project to ensure its innovation. It is incumbent upon the researchers to thoroughly research and reference the knowledge base in order to guarantee that the designs produced are research contributions and not routine designs based upon the application of well-known processes. The central design cycle iterates between the core activities of building and evaluating the design artifacts and processes of the research.
== Ethical issues == DSR in itself implies an ethical change from describing and explaining of the existing world to shaping it. One can question the values of information system research, i.e., whose values and what values dominate it, emphasizing that research may openly or latently serve the interests of particular dominant groups. The interests served may be those of the host organization as perceived by its top management, those of information system users, those of information system professionals or potentially those of other stakeholder groups in society.
== Academic Examples of Design Science Research == There are limited references to examples of DSR, but Adams has completed two PhD research topics using Peffers et al.'s DSRP (both associated with digital forensics but from different perspectives): 2013: The Advanced Data Acquisition Model (ADAM): A process model for digital forensic practice 2024: The Advanced Framework for Evaluating Remote Agents (AFERA): A Framework for Digital Forensic Practitioners
== See also == Empirical research Action research Participant observation Case study Design thinking
== References ==
== Research examples == Adams, R., Hobbs, V., Mann, G., (2013). The Advanced Data Acquisition Model (ADAM): A process model for digital forensic practice. URL: http://researchrepository.murdoch.edu.au/id/eprint/14422/2/02Whole.pdf
== Further reading == March, S. T., Smith, G. F., (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), pp. 251–266. March, S. T., Storey, V. C., (2008). Design Science in the Information Systems Discipline: An introduction to the special issue on design science research, MIS Quarterly, Vol. 32(4), pp. 725–730. Mettler T, Eurich M, Winter R (2014). "On the Use of Experiments in Design Science Research: A Proposition of an Evaluation Framework". Communications of the AIS. 34 (1): 223–240. Opdenakker, Raymond en Carin Cuijpers (2019),’Effective Virtual Project Teams: A Design Science Approach to Building a Strategic Momentum’, Springer Verlag. Van Aken, J. E. (2004). Management Research Based on the Paradigm of the Design Sciences: The Quest for Field-Tested and Grounded Technological Rules. Journal of Management Studies, 41(2), 219–246. Watts S, Shankaranarayanan G., Even A. Data quality assessment in context: A cognitive perspective. Decis Support Syst. 2009;48(1):202-211.
== External links == Design Science Research in Information System and Technology community