Research Perspectives: Design Theory Indeterminacy: What Is it, How Can it Be Reduced, and Why Did the Polar Bear Drown?
Bibliographic record
Abstract
Design science research strives to be practical and relevant. Yet few researchers have examined the extent to which practitioners can meaningfully utilize theoretical knowledge produced by design science research in solving concrete real-world problems. Are design theories developed by scientists readily amenable to application by practitioners? Does the application of a theory by practitioners always lead to the outcomes predicted (by the scientists)? We examine a particularly difficult challenge—ensuring that the development and deployment of an IT artifact by practitioners based on a design theory result in appropriate changes in the environment predicted by the design theory. As we show in our paper, a gulf exists between theoretical propositions and concrete issues faced in practice—a challenge we refer to as design theory indeterminacy. Design theory indeterminacy might result in considerable ambiguity when implementing a design theory in practice and reduce the potential relevance of information systems knowledge. In this paper, we articulate the problem of design theory indeterminacy, examine factors that contribute to it, and suggest fruitful directions for future research to help reduce it.
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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 | 0.113 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.023 | 0.038 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".