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© Know-Center GmbH, www.know-center.at
Design Science Research in
Information Systems
Dipl.-Ing. Angela Fessl
UPC-Team...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design
• Design means „to invent and ...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DESIGN SCIENCE
RESEARCH CYCLE
3
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research
„The goal is ...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Guidelines
Gu...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Checklist for Design Science Research...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Checklist for Design Science Research...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Relevance Cycle Design
Cycle
Rigor Cy...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Cycle
Relevan...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Application Domain
• People
• Organis...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Cycle
Rigor C...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Cycle
Rigor C...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Foundations
• Scientific Theories &
M...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Cycle
Design ...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Cycle
Design ...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Evaluate
Foundations
• Scientific The...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DESIGN SCIENCE
RESEARCH PROCESS
MODEL...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Process Model...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DSRP Model: Awareness of Problem
• In...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DSRP Model: Suggestion
• Suggestion p...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DSRP Model: Development
• Tentative D...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DSRP Model: Evaluation
• Evaluation o...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
DSRP Model: Conclusion
• End of resea...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Design Science Research Process Model...
© Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
References
• Vaishnavi, V., & Kuechle...
© Know-Center GmbH
gefördert durch das Programm COMET (Competence Centers for Excellent Technologies), wir danken unseren ...
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Design Science Research

Contains a summary of design science research:
Hevner: Design Science Research Cycle
Vaishnavi: Descign Science Research Process

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Design Science Research

  1. 1. b b © Know-Center GmbH, www.know-center.at Design Science Research in Information Systems Dipl.-Ing. Angela Fessl UPC-Team – Research Methods
  2. 2. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design • Design means „to invent and to bring into being“ (Websters Dictionary and Thesaurus, 1992) • Design is… • when creating new artifacts that do not exist. • Design is routine… • If the knowledge required for creating the artifact exists • Design is innovative … • If the knoweldge for creating the artifact does not exist • Innovative design • call for research (design science research) to fill the knowledge gaps and result in research publication(s) or patent(s). (Vaishnavi et al., 2004) 2
  3. 3. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DESIGN SCIENCE RESEARCH CYCLE 3
  4. 4. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research „The goal is to enhance our understanding of what it means to do high quality design research in information systems….“ 4 (Hevner et al. 2004; Hevner 2007; Hevner, 2010; )
  5. 5. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Guidelines Guideline Description Guideline 1: Design as Artifact Design-science research must produce a viable artifact in the form of a construct, a model, a method or an instantiation. Guideline 2: Problem Relevance The objective of design-science research is to develop technology- based solutions to important and relevant business problems. Guideline 3: Design Evaluation The utility, quality and efficacy of a design artifact must be rigorously demonstrated via well-executed evaluation methods. Guideline 4: Research Contribution Effective design-science research must provide clear and verifiable contributions in the areas of the design artifact, design foundations and/or design methodologies. Guideline 5: Research Rigor Design-science research relies upon the application of rigorous methods in both the construction and evaluation of the design artifact. Guideline 6: Design as a Search Process The search for an effective artifact requires utilizing available means to reach desired ends while satisfying laws in the problem environment. Guideline 7: Communication of Research Design-science research must be presented effectively both to technology-oriented as well as management-oriented audiences. 5
  6. 6. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Checklist for Design Science Research • 1. What is the research question (design requirements)? • 2. What is the artifact? How is the artifact represented? • 3. What design processes (search heuristics) will be used to build the artifact? • 4. How are the artifact and the design processes grounded by the knowledge base? What, if any, theories support the artifact design and the design process? 6
  7. 7. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Checklist for Design Science Research • 5. What evaluations are performed during the internal design cycles? What design improvements are identified during each design cycle? • 6. How is the artifact introduced into the application evironment and how is it field tested? What metrics are used to demonstrate artifact utility and improvement over previous artifacts? • 7. What new knowledge is added to the knowledge base and in what form? • 8. Has the research question been satisfactorily addressed? 7
  8. 8. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Relevance Cycle Design Cycle Rigor Cycle Design Science Research Cycle
  9. 9. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Cycle Relevance Cycle • Provides the application environment • Users, organisational and technical systems • Problems and Opportunities • Goal: improvement of the application environment with • The development of new and innovative artefacts • Processes for building these artefacts • Relevance Cycle • Provides the requirements for research • Defines acceptance criteria for the ultimate evaluation of the research results • Field studies show • Deficiencies in functionality of the artefact • Adapt requirements to the artefact 9
  10. 10. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Application Domain • People • Organisational Systems • Technical Systems • Problems & Opportunities Relevance Cycle Requirements Field Testing Design Cycle Rigor Cycle Design Science Research Cycle
  11. 11. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Cycle Rigor Cycle • Adds past knowledge to the research project • Knowledge base consists of • Experiences and expertise defining the state-of-the-art • Existing artifacts and processes found in the applicaton domain • Researchers need to research and reference the existing knowledge base to guarantee that the designs produced are new research contributions. 11
  12. 12. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Cycle Rigor Cycle • Researchers have to • select appropriate theories and methods for constructing and evaluating the artifact. • Contribute to the knowledge base (e.g. new methods, theories) • Essential to selling the research to an academic audience • Attract other practitioner audiences (not only the original environment) 12
  13. 13. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Foundations • Scientific Theories & Methods • Experience & Expertise • Meta-Artifacts (Design Products & Design Processes) Application Domain • People • Organisational Systems • Technical Systems • Problems & Opportunities Relevance Cycle Requirements Field Testing Design Cycle Rigor Cycle Grounding Additions to KB Design Science Research Cycle
  14. 14. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Cycle Design Cycle • Heart of any design science research project. • It iterates between • the construction of an artefact, • its evaluation and • subsequent feedback to refine the design further. • Input: • Requirements come from the Relevance Cycle • Design, evaluation theories and methods come from the Rigor Cycle 14
  15. 15. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Cycle Design Cycle • Performance of the design cycle • Maintaining the balance between construction and evaluation • Both must be based on relevance and rigor • Artifacts must be • Thoroughly tested in laboratory and experimental settings • before being released in a field test • Output: • Contribution to the relevance cycle • Contribution to the rigor cycle „The essence of Information Systems as design science lies in the scientific evaluation of artifacts.“ (Juhani, 2007) 15
  16. 16. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Evaluate Foundations • Scientific Theories & Methods • Experience & Expertise • Meta-Artifacts (Design Products & Design Processes) Build Design Artifacts & Processes Application Domain • People • Organisational Systems • Technical Systems • Problems & Opportunities Relevance Cycle • Requirements • Field Testing Design Cycle Rigor Cycle • Grounding • Additions to KB Design Science Research Cycle
  17. 17. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DESIGN SCIENCE RESEARCH PROCESS MODEL 17
  18. 18. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Process Model 18 (Vaishnavi and Kuechler, 2004)
  19. 19. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DSRP Model: Awareness of Problem • Interesting research problem from multiple sources e.g. new developments • Reading research publications (e.g. allied fields) • Opportunity for appliation of new findings in own research area • Outcome: Proposal for new research effort 19
  20. 20. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DSRP Model: Suggestion • Suggestion phase is closely connected to proposal and tentative design • Creative step • Envision new functionality on new or new and existing elements • Outcome: Input for Development 20
  21. 21. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DSRP Model: Development • Tentative Design is further developed and implemented • The implementation need not involve novelty beyond the state-of-practice for the given artifact • Novelty is primary in the design (not in the construction) • Outcome: Input for Evaluation 21
  22. 22. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DSRP Model: Evaluation • Evaluation of the artifact already defined in the proposal • Hypothesis were made about the behaviour of the artifact. • Deviations from expectations, (qualitative and quantitative), must be tetatively explained. • Analysis confirms or contradicts hypothesis -> things are getting interesting • Evaluation results and additional information gained in construction and running of the artifact are brought together and fed back to another round of suggetion. 22
  23. 23. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics DSRP Model: Conclusion • End of research cycle or final of a specific research effort -> results are „good enough“ • Results: Knowledge gained is either • „firm“ – facts have been learng and can be repeated • „loose ends“ – anomalous behaviour that needs further explanation • Communication is important • Knowledge Contribution 23
  24. 24. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics Design Science Research Process Model 24 (Vaishnavi and Kuechler, 2004)
  25. 25. © Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics References • Vaishnavi, V., & Kuechler, W. (2004). Design science research in information systems. • Hevner, Alan, R., March, Salvatore, T., Park, J., and Ram, S. Design science in information research. MIS Quarterly 28, 1 (March 2004), 75–1005. • Hevner, Alan, R. A three cycle view of design science research. Scandinavian Journal of Information Science. 19, 2 (2007). • Hevner, Alan, R., and Chatterjee, S. Design science research in information systems. Integrated Series in Information Systems 22 (2010), 9–22.
  26. 26. © Know-Center GmbH gefördert durch das Programm COMET (Competence Centers for Excellent Technologies), wir danken unseren Fördergebern:

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