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Design science (methodology)

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133:, computer scientists have been doing DSR without naming it. They have developed new architectures for computers, new programming languages, new compilers, new algorithms, new data and file structures, new data models, new database management systems, and so on. Much of the early research was focused on systems development approaches and methods. The dominant research philosophy in many disciplines has focused on developing cumulative, theory-based research results in order to make prescriptions. It seems that this ‘theory-with-practical-implications’ research strategy has not delivered on this aim, which led to search for practical research methods such as DSR. 33:(DSR) is a research paradigm focusing on the development and validation of prescriptive knowledge in information science. Herbert Simon distinguished the natural sciences, concerned with explaining how things are, from design sciences which are concerned with how things ought to be, that is, with devising artifacts to attain goals. Design science research methodology (DSRM) refers to the research methodologies associated with this paradigm. It spans the methodologies of several research disciplines, for example 142:
iterated a number of times before the final design artifact is generated. In DSR, the focus is on the so-called field-tested and grounded technological rule as a possible product of Mode 2 research with the potential to improve the relevance of academic research in management. Mode 1 knowledge production is purely academic and mono-disciplinary, while Mode 2 is multidisciplinary and aims at solving complex and relevant field problems.
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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
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that professionals of the discipline in question can use to design solutions for their field problems. Design sciences focus on the process of making choices on what is possible and useful for the creation of possible futures, rather than on what is currently existing. This mission can be compared to
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Transparency in DSR is becoming an emerging concern. DSR strives to be practical and relevant. Yet few researchers have examined the extent to which practitioners can meaningfully utilize theoretical knowledge produced by DSR in solving concrete real-world problems. There is a potential gulf between
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the one of the ‘explanatory sciences’, like the natural sciences and sociology, which is to develop knowledge to describe, explain and predict. Hevner states that the main purpose of DSR is achieving knowledge and understanding of a problem domain by building and application of a designed artifact.
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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.
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disciplines, though is not restricted to these and can be found in many disciplines and fields. DSR, or constructive research, in contrast to explanatory science research, has academic research objectives generally of a more pragmatic nature. Research in these disciplines can be seen as a quest for
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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
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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,
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for the specified problem. In order to form a novel research contribution, the artifact must either solve a problem that has not yet been solved, or provide a more effective solution. Both the construction and evaluation of the artifact must be done rigorously, and the results of the research
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The design process is a sequence of expert activities that produces an innovative product. The artifact enables the researcher to get a better grasp of the problem; the re-evaluation of the problem improves the quality of the design process and so on. This build-and-evaluate loop is typically
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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.
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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).
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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”.
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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.
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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):
429:, Kuechler, W. (2004/21). “Design Science Research in Information Systems” January 20, 2004 (updated in 2017 and 2019 by Vaishnavi, V. and Stacey, P.; last updated November 24, 2021. URL: 861: 195:
Effective design-science research must provide clear and verifiable contributions in the areas of the design artifact, design foundations, and/or design methodologies.
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Aparicio, J.T.; Aparicio, M.; Costa, C.J. (2023). "Design Science in Information Systems and Computing". In Anwar, S.; Ullah, A.; Rocha, Á.; Sousa, M.J. (eds.).
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Opdenakker, Raymond en Carin Cuijpers (2019),’Effective Virtual Project Teams: A Design Science Approach to Building a Strategic Momentum’, Springer Verlag.
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Van Aken, J. E. (2004). Management Research Based on the Paradigm of the Design Sciences: The Quest for Field-Tested and Grounded Technological Rules.
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March, S. T., Smith, G. F., (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), pp. 251–266.
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Design science is a valid research methodology to develop solutions for practical engineering problems. Design science is particularly suitable for
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with the explicit intention of improving the functional performance of the artifact. DSRM is typically applied to categories of artifacts including
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Van Aken JE (2005). "Management research as a design science: Articulating the research products of mode 2 knowledge production in management".
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The search for an effective artifact requires utilizing available means to reach desired ends while satisfying laws in the problem environment.
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Storey, 2008), new explanatory theories, new design and developments models and implementation processes or methods (Ellis & Levy 2010).
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Watts S, Shankaranarayanan G., Even A. Data quality assessment in context: A cognitive perspective. Decis Support Syst. 2009;48(1):202-211.
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Hevner, A. R.; March, S. T.; Park, J. & Ram, S. Design Science in Information Systems Research. MIS Quarterly, 2004, 28, 75-106. URL:
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https://www.researchgate.net/publication/258224615_The_Advanced_Data_Acquisition_Model_ADAM_A_process_model_for_digital_forensic_practice
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Adams, R., Hobbs, V., Mann, G., (2013). The Advanced Data Acquisition Model (ADAM): A process model for digital forensic practice. URL:
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Design-science research relies upon the application of rigorous methods in both the construction and evaluation of the design artifact.
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The objective of design-science research is to develop technology-based solutions to important and relevant business problems.
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The utility, quality, and efficacy of a design artifact must be rigorously demonstrated via well-executed evaluation methods.
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Design-science research must be presented effectively both to technology-oriented as well as management-oriented audiences.
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https://espace.curtin.edu.au/bitstream/handle/20.500.11937/93974/Adams%20RB%202023%20Public.pdf?sequence=1&isAllowed=y
757:"Research Perspectives: Design Theory Indeterminacy: What Is it, How Can it Be Reduced, and Why Did the Polar Bear Drown?" 236:
The engineering cycle is a framework used in Design Science for Information Systems and Software Engineering, proposed by
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Design-science research must produce a viable artifact in the form of a construct, a model, a method, or an instantiation.
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HEC Montréal, Canada; Lukyanenko, Roman; Parsons, Jeffrey; Memorial University of Newfoundland, Canada (2020-09-01).
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iterates between the core activities of building and evaluating the design artifacts and processes of the research.
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2024: The Advanced Framework for Evaluating Remote Agents (AFERA): A Framework for Digital Forensic Practitioners
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Watts S; Shankaranarayanan G & Even A (2009). "Data quality assessment in context: A cognitive perspective".
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Markus ML; Majchrzak A & Gasser L. "A design theory for systems that support emergent knowledge processes".
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2013: The Advanced Data Acquisition Model (ADAM): A process model for digital forensic practice
909:"On the Use of Experiments in Design Science Research: A Proposition of an Evaluation Framework" 34: 430: 446:(2008). "On theory development in design science research: Anatomy of a research project". 8: 86: 78: 546:
Peffers, Ken; Tuunanen, Tuure; Rothenberger, Marcus A.; Chatterjee, Samir (2007-12-01).
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Iivari J (2007). "A paradigmatic analysis of information systems as a design science".
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Proceedings of the International Conference on Information Technology and Applications
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theoretical propositions and concrete issues faced in practice—a challenge known as
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Hevner et al. have presented a set of guidelines for DSR within the discipline of
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Dresch, Aline; Lacerda, Daniel Pacheco; Valle, José AntÎnio Jr. Antunes (2015).
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Design science methodology for information systems and software engineering
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https://citeseerx.ist.psu.edu/pdf/7d02dc5c8c0b316e592244c441796e6ad31d8bff
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Design Science Research: A Method for Science and Technology Advancement
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http://researchrepository.murdoch.edu.au/id/eprint/14422/2/02Whole.pdf
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Hevner AR (2007). "The three cycle view of design science research".
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DSR focuses on the development and performance of (designed)
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Design Science Research in Information System and Technology
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http://desrist.org/design-research-in-information-systems
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presented effectively both to technology-oriented and
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Thousand Oaks, Calif.: SAGE. p. 2. 226:The engineering cycle and the design cycle 146:Guidelines in information systems research 842: 685: 683: 552:Journal of Management Information Systems 514: 512: 510: 793: 689: 836: 448:European Journal of Information Systems 435: 351: 14: 952: 907:Mettler T, Eurich M, Winter R (2014). 729: 680: 507: 170:Hevner counts 7 guidelines for a DSR: 702: 631: 376: 270: 881: 116:The main goal of DSR is to develop 93:'s Software Engineering Institute, 24: 894: 136: 64:, design methodologies (including 25: 971: 937: 593:Hevner; March; Park; Ram (2004). 354:Encyclopedia of management theory 283: 533:10.1111/j.1467-8551.2005.00437.x 866: 855: 787: 748: 89:'s Center for Design Research, 634:The sciences of the artificial 625: 586: 539: 485:. Cham: Springer. pp. i. 474: 370: 345: 53: 13: 1: 928:Journal of Management Studies 521:British Journal of Management 338: 111: 674:10.1007/978-981-19-9331-2_35 250:Human factors and ergonomics 243: 77:understanding and improving 7: 306: 220:design theory indeterminacy 205:Design as a search process: 10: 976: 564:10.2753/MIS0742-1222240302 377:Simon, Herbert A. (1988). 247: 229: 211:Communication of research: 125:Evolution and applications 91:Carnegie Mellon University 49:within research projects. 913:Communications of the AIS 723:10.1016/j.dss.2009.07.012 491:10.1007/978-3-319-07374-3 62:human/computer interfaces 129:Since the first days of 99:Brunel University London 37:, which offers specific 794:Wieringa, Roel (2014). 323:Participant observation 193:Research contributions: 31:Design science research 175:Design as an artifact: 35:information technology 167:-oriented audiences. 18:Constructive research 773:10.17705/1jais.00639 632:Simon, H.A. (1969). 460:10.1057/ejis.2008.40 352:Kessler, EH (2013). 27:Research methodology 152:Information Systems 87:Stanford University 711:Decis Support Syst 313:Empirical research 271:A three-cycle view 187:Design evaluation: 181:Problem relevance: 930:, 41(2), 219–246. 882:Research examples 807:978-3-662-43839-8 500:978-3-319-07373-6 254:Human reliability 232:Engineering cycle 79:human performance 16:(Redirected from 967: 960:Research methods 920: 875: 870: 864: 859: 853: 852: 840: 834: 833: 827: 819: 791: 785: 784: 767:(5): 1343–1369. 752: 746: 745: 733: 727: 726: 706: 700: 699: 687: 678: 677: 661: 655: 649: 638: 637: 629: 623: 622: 611:10.2307/25148625 590: 584: 583: 543: 537: 536: 516: 505: 504: 478: 472: 471: 439: 433: 424: 415: 414: 374: 368: 367: 349: 131:computer science 74:Computer Science 21: 975: 974: 970: 969: 968: 966: 965: 964: 950: 949: 940: 897: 895:Further reading 884: 879: 878: 871: 867: 860: 856: 841: 837: 821: 820: 808: 792: 788: 753: 749: 734: 730: 707: 703: 688: 681: 662: 658: 650: 641: 630: 626: 591: 587: 544: 540: 517: 508: 501: 479: 475: 440: 436: 425: 418: 395:10.2307/1511391 389:(1/2): 67–82 . 375: 371: 364: 350: 346: 341: 333:Design thinking 318:Action research 309: 295: 286: 273: 256: 246: 234: 228: 199:Research rigor: 148: 139: 137:Characteristics 127: 114: 28: 23: 22: 15: 12: 11: 5: 973: 963: 962: 948: 947: 939: 938:External links 936: 935: 934: 931: 924: 921: 904: 901: 896: 893: 892: 891: 883: 880: 877: 876: 865: 854: 835: 806: 800:. 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Index

Constructive research
information technology
guidelines
evaluation
iteration
artifacts
algorithms
human/computer interfaces
process models
Engineering
Computer Science
human performance
MIT Media Lab
Stanford University
Carnegie Mellon University
Xerox
Brunel University London
knowledge
computer science
Information Systems
problem domain
utility
management
Engineering cycle
Roel Wieringa
Human factors and ergonomics
Human reliability
design cycle
Empirical research
Action research

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