Positioning and Presenting Design Science Research for Maximum Impact: A Comprehensive Guide
Based on Gregor, S., & Hevner, A. R. (2013), “Positioning and Presenting Design Science Research for Maximum Impact”
Design Science Research (DSR) has firmly established itself as a legitimate and vital paradigm within Information Systems (IS). It is responsible for creating socio-technical artifacts—such as decision support systems, modeling tools, and governance strategies—that define the field. However, despite its importance, DSR often fails to achieve its full potential impact due to a fundamental communication problem. Researchers frequently struggle to clearly define, position, and communicate their unique contributions to knowledge, leading to confusion among editors, reviewers, and readers.
This article provides a comprehensive breakdown of the framework developed by Shirley Gregor and Alan Hevner to address this challenge. It explains how to position research based on the maturity of the problem and the solution, and how to structure publications to clearly demonstrate rigor and relevance.
1. The Core Challenge: Three Questions Every Researcher Must Answer
Regardless of research paradigm, every scholarly contribution is ultimately judged by three timeless questions, famously attributed to mathematician G. H. Hardy:
- Is it true? (Is the method sound and the evidence credible?)
- Is it new? (Does it offer a genuine advance over prior work?)
- Is it interesting? (Does it address a substantial and meaningful problem?)
Gregor and Hevner argue that the third question—“Is it interesting?”—is arguably the most critical. If the work is not interesting, the first two questions become irrelevant. Successful DSR must convincingly answer all three.
2. Understanding DSR Knowledge: Omega and Lambda
To properly position a DSR contribution, researchers must understand the two distinct types of knowledge involved:
- Descriptive Knowledge (Omega):
This is “what” knowledge. It describes natural or social phenomena, including behavioral theories, regularities, and explanatory models. - Prescriptive Knowledge (Lambda):
This is “how” knowledge. It concerns human-designed artifacts such as constructs, models, methods, algorithms, and system instantiations.
Effective DSR operates as a cycle of knowledge consumption and production. Researchers draw from existing Omega and Lambda knowledge to ground their work, and through the design process, they generate new knowledge that is contributed back to these knowledge bases.
3. The DSR Knowledge Contribution Framework
The centerpiece of the paper is the DSR Knowledge Contribution Framework, a 2 × 2 matrix that helps researchers position their work based on:
- the maturity of the application domain (problem), and
- the maturity of existing solutions.
The Four Quadrants of Contribution
1. Invention: New Solutions for New Problems
Context: Low application domain maturity / low solution maturity
Definition:
A radical breakthrough or clear departure from existing ways of thinking. This occurs when little is known about the problem context and no effective solutions currently exist.
Challenge:
These contributions are rare. Researchers often must conceptualize the problem itself, as relevant research questions may not yet exist.
Example:
The first data-mining algorithm for association rules (Agrawal et al., 1993), which created an entirely new research field.
2. Improvement: New Solutions for Known Problems
Context: High application domain maturity / low solution maturity
Definition:
The creation of more efficient or effective artifacts for a well-understood problem domain.
Challenge:
Researchers must clearly demonstrate that the new solution represents a genuine improvement over existing artifacts. This requires rigorous evaluation to establish superiority in areas such as efficiency, quality, or productivity.
Prevalence:
This is the most common form of DSR in Information Systems research.
3. Exaptation: Known Solutions Extended to New Problems
Context: Low application domain maturity / high solution maturity
Definition:
The adaptation of existing artifacts that are effective in one domain to solve problems in a new and different application area.
Challenge:
The researcher must show that the extension is non-trivial. The new context must introduce unique challenges that make the adaptation both difficult and interesting.
Example:
Applying intelligent agent technologies, originally developed for other purposes, to the design of automated kiosks for deception detection.
4. Routine Design: Known Solutions for Known Problems
Context: High application domain maturity / high solution maturity
Definition:
The application of established solutions to familiar problems using well-known practices.
Status:
This is not considered research. While it represents high-quality professional practice, it does not contribute new knowledge to either the Omega or Lambda knowledge bases.
4. Levels of Artifact Abstraction
DSR contributions are not limited to working software. Gregor and Hevner identify three levels of artifact abstraction that constitute valid research contributions:
- Level 1: Situated Implementation (Instantiations)
Concrete systems or processes implemented in a specific context. - Level 2: Nascent Design Theory
Abstracted knowledge such as constructs, models, methods, and design principles that can be applied across contexts. - Level 3: Well-Developed Design Theory
Mid-range or grand theories explaining embedded phenomena across multiple settings.
Researchers are encouraged to push their contributions toward higher levels of abstraction (Levels 2 or 3) to enhance generalizability and increase the perceived “interestingness” of their work.
5. Structuring the DSR Publication
To effectively communicate DSR contributions, Gregor and Hevner propose a Publication Schema tailored specifically for design science. This structure replaces the traditional “Results” section found in behavioral research with a detailed Artifact Description.
Recommended Structure
- Introduction:
Define the problem and the goals (meta-requirements) of the artifact. Clearly identify the class of problems and establish practical relevance. - Literature Review:
Review prior Omega (descriptive theories) and Lambda (existing artifacts) knowledge to demonstrate novelty. - Method:
Explain the DSR methodology used and justify the chosen design and evaluation methods. - Artifact Description:
The core of the paper. Describe the design process and the artifact itself. This section replaces the traditional “Results” section. - Evaluation:
Provide evidence of validity, utility, quality, and efficacy using methods such as case studies, experiments, simulations, or expert reviews. - Discussion:
Interpret findings, abstract design principles or nascent theory, and clearly restate the claim of novelty. - Conclusion:
A concise “declaration of victory” summarizing the key contributions and their significance for research and practice.
Conclusion
The key to achieving maximum impact in Design Science Research lies in clarity. By using the Knowledge Contribution Framework to properly position their work—whether as invention, improvement, or exaptation—and by following the DSR Publication Schema, researchers can convincingly address the critical questions of truth, novelty, and interest. Doing so ensures that DSR not only solves immediate practical problems but also contributes enduring knowledge to the Information Systems discipline.
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