Mapping Design Contributions in Information Systems Research: The Design Research Activity Framework
Bibliographic record
Abstract
Despite growing interest in design science research in information systems, our understanding about what constitutes a design contribution and the range of research activities that can produce design contributions remains limited. We propose the design research activity (DRA) framework for classifying design contributions based on the type of statements researchers use to express knowledge contributions and the researcher role with respect to the artifact. These dimensions combine to produce a DRA framework that contains four quadrants: construction, manipulation, deployment, and elucidation. We use the framework in two ways. First, we classify design contributions that the Journal of the Association for Information Systems (JAIS) published from 2007 to 2019 and show that the journal published a broad range of design research across all four quadrants. Second, we show how one can use our framework to analyze the maturity of design-oriented knowledge in a specific field as reflected in the degree of activity across the different quadrants. The DRA framework contributes by showing that design research encompasses both design science research and design-oriented behavioral research. The framework can help authors and reviewers assess research with design implications and help researchers position and understand design research as a journey through the four quadrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.230 | 0.272 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.053 | 0.038 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.031 | 0.045 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".