It's All in the Name: Failure-Induced Learning by Multiunit Chains
Bibliographic record
Abstract
We examine factors leading multiunit chains to adopt a common naming strategy, that is, naming components in a manner that identifies them with each other and the overall chain, rather than a local naming strategy that identifies a chain's components with their locations but not each other. Because chains' naming strategies have been shown to be critical to their success, we examine the effects of component failures on naming strategies. We advance organizational and interorganizational learning processes to explain chains' adoption of local naming strategies, which stress local adaptation, or common naming strategies, which emphasize standardization. In contrast to past research emphasizing learning from success, we focus on learning from the failure of strategy, specifically, the failure of a chain's own and other chains' commonly and locally named components. Two fundamental results emerge from our analysis of Ontario nursing home chains' naming strategies from 1971 to 1996. One is that nursing home chains learned from their own and others' failures, and the second is that the chains learned less from failures when they had a historical investment in the failing strategy.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".