Assurer Reputation for Competence in a Multiservice Context*
Bibliographic record
Abstract
We experimentally examine determinants of the transferability and durability of an assurer's flagship-service reputation for competence in a multi-service context. Economic and psychology theories agree that transferability will increase when a new service and a flagship service require relatively similar, as opposed to relatively dissimilar, competencies. These theories disagree, however, regarding the durability of an assurer's flagship-service reputation following a performance failure in a new service. Economic theory requires some reciprocity for transferability and durability, and, accordingly, predicts that a new-service failure will inflict more damage on a flagship-service reputation when the new and flagship services require relatively similar, as opposed to relatively dissimilar, competencies. Psychology theory, in contrast, predicts that a new-service failure will tend not to damage the flagship-service reputation, even when the new and flagship services require relatively similar competencies. Thus, unlike economic theory, psychology theory predicts that relatively similar competencies between a new and a flagship service can improve the transferability of an assurer's flagship-service reputation for competence without reciprocally threatening its durability. Experimental findings indicate that reputation transferability increases when the new and flagship services require relatively similar, as opposed to relatively dissimilar, competencies. They also indicate that a new-service failure damages the flagship-service reputation only when, and to a greater degree when, the new and flagship services require relatively dissimilar, as opposed to relatively similar, competencies. Thus, empirically, greater similarity in the competencies required by a new and flagship service simultaneously enhances the transferability and durability of the assurer's flagship-service reputation. These empirical findings are better explained by psychology theory than by economic theory.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".