The State of Design Science Research within the BISE Community: An Empirical Investigation
Bibliographic record
Abstract
The Business & Information Systems Engineering (BISE) community in the German-speaking countries has a long track record of publishing papers using design science research (DSR). However, the state of recent DSR within the BISE community is not well documented and the lessons learned can be useful for other communities. This paper investigates the use of DSR methodology by examining articles published in the BISE community’s primary outlets. We focus on understanding the artifacts created, the foundations for building these artifacts, and the evaluation methods used. The results reveal a) a broad view of foundations for DSR by incorporating artifacts that are used in practice, b) the focus on the organization as the unit of analysis, c) a pluralism of research methods that cater to the timeliness of problems addressed, and d) low level of theoretical underpinnings, thus lacking in DSR rigor aspects.
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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 | 0.340 | 0.435 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".