A Review of Design Science Research in Information Systems: Concept, Process, Outcome, and Evaluation
Bibliographic record
Abstract
Design science research is a research paradigm focusing on problem-solving. It is increasingly accepted and adopted by Information Systems (IS) researchers as a legitimate research paradigm because of its capability in balancing research relevance and rigor. In the last fifteen years, many design science research has been published in top IS journals and has received a lot of attentions from IS researchers. However, current confusion and misunderstandings of DSR’s central ideas (e.g., definition, philosophical foundation, research outcomes, etc.) are obstructing it from having a more striking influence on the IS field. The purpose of this paper is to present a comprehensive and critical review of existing DSR literature. In total, 119 papers, published in top IS journals and conference proceedings, were included in the review. The results of this study portray a big picture of current DSR in IS field and build a comprehensive theoretical knowledge base in terms of DSR-related issues. This study also identifies many research issues which can be examined by future DSR. Available at: https://aisel.aisnet.org/pajais/vol10/iss1/2/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".