Shifting Logics of Legitimation in the Diffusion of Complex Innovations
Bibliographic record
Abstract
Legitimation and competition are two major forces moulding organizational field and the diffusion of innovations. While discursive legitimation provides "rational justifications" for innovations, competition may incite organizations to acquire effective innovations preemptively. This paper draws on a case study of the legitimation and diffusion of a sophisticated medical technology to suggest that, in highly regulated environments, these two forces may interact, and that opposing legitimation strategies may be associated with competition. We argue that while convergent discursive legitimation strategies tend to speed up the diffusion process, divergent discursive legitimation strategies may have the opposite effect. The case suggests that the dominant logics of legitimation may shift, oscillating between convergence and divergence as an innovation diffuses. We also show how the resulting delays in diffusion may be pre-empted by a phenomenon we call institutional delinquency, that is when the moral and cognitive-cultural legitimacies of the technology among professionals and managers becomes sufficient to counteract regulatory forces. [Authors]
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".