Performance Feedback Persistence: Comparative Effects of Historical Versus Peer Performance Feedback on Innovative Search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Firms use aspirations to regulate innovative search activities, but peer and historical referents may contain different signals regarding performance feedback. Integrating insights from the literature on profit persistence with the behavioral theory of the firm, we propose a persistence-based framework of organizational innovative search that connects the persistence characteristics of feedback from peer and historical referents with innovative search. We first predict that feedback from peer referents is more persistent than feedback from historical referents. Further, we theorize that peer performance feedback produces more pronounced effects: Performance above (below) peer aspiration leads to less (more) innovative search compared with performance above (below) the historical aspiration level. In addition, because industries impose heterogeneous levels of profit persistence, the differential effect between peer and historical performance feedback on innovative search is likely to be more evident in highly persistent industries. Examining the research-and-development intensity of a comprehensive panel of Compustat manufacturing firms over the past 45 years, our results from quasi–maximum likelihood analysis and fixed-effect panel regression largely support our theoretical development. Our study extends a nascent understanding of aspiration heterogeneity by revealing and empirically confirming the critical role of persistence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it