The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Cross-Evaluation: A new model for information system evaluation
Journal of the American Society for Information Science and Technology
A Design Science Research Methodology for Information Systems Research
Journal of Management Information Systems
Design Science Research Methods and Patterns: Innovating Information and Communication Technology
Design Science Research Methods and Patterns: Innovating Information and Communication Technology
Building theory in the sciences of the artificial
Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology
Design and natural science research on information technology
Decision Support Systems
A design science based evaluation framework for patterns
ACM SIGMIS Database
Criteria of progress for information systems design theories
Information Systems and e-Business Management
Design science in information systems research
MIS Quarterly
MIS Quarterly
Design science in practice: designing an electricity demand response system
DESRIST'13 Proceedings of the 8th international conference on Design Science at the Intersection of Physical and Virtual Design
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The central outcome of design science research (DSR) is prescriptive knowledge in the form of IT artifacts and recommendations. However, prescriptive knowledge is considered to have no truth value in itself. Given this assumption, the validity of DSR outcomes can only be assessed by means of descriptive knowledge to be obtained at the conclusion of a DSR process. This is reflected in the build-evaluate pattern of current DSR methodologies. Recognizing the emergent nature of IT artifacts this build-evaluate pattern, however, poses unfavorable implications regarding the achievement of rigor within a DSR project. While it is vital in DSR to prove the usefulness of an artifact a rigorous DSR process also requires justifying and validating the artifact design itself even before it has been put into use. This paper proposes three principles for evaluating DSR artifacts which not only address the evaluation of an artifact's usefulness but also the evaluation of design decisions made to build an artifact. In particular, it is argued that by following these principles the prescriptive knowledge produced in DSR can be considered to have a truth-like value.