Exploring the Scientific Impact of Information Systems Design Science Research
Bibliographic record
Abstract
While design science research has established its position as a prominent field of research in the IS community, there is a lack of transparency regarding the impact of recent information systems design science research (IS DSR) papers. This lack of insight arguably poses challenges to an informed discourse and limits our ability to communicate the progress that IS DSR has achieved. Therefore, after mapping impactful IS DSR papers, we develop a scientometric study to address the lack of insights into factors that affect the scientific impact of IS DSR papers in top IS journals. In this study, we focus on active, IS-specific DSR areas and consider papers published in the AIS Senior Scholars’ basket of journals between 2004 and 2014. Specifically, we develop a model that explores factors that affect IS DSR papers’ scientific impact. Our findings show that theorization and novelty significantly explain scientific impact. We discuss our work’s implications and derive recommendations intended to shape future knowledge creation in IS DSR.
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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 | 0.101 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.059 | 0.100 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".