An Empirical Analysis of the Incentive-Action-Performance Chain of the Principal-Agent Model
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
ABSTRACT: This study empirically investigates the incentive-action-performance chain on cross-sectional plant data in the context of a just-in-time (JIT- plant manufacturing environment. Incentives in this study are of the “soft” goal-oriented variety rather than direct compensation. The empirical analysis is implemented using ordinary least squares and Heckman two-stage regressions to account for the potential endogeneity of the JIT adoption decision. We find that plant performance outcomes are associated with actions, namely, the breadth and intensities of plant JIT practices adopted by plant management, but are not associated with performance incentives. However, we find that the JIT adoption decision is associated with incentives. We further find that it is the essential inventory incentive aspects of JIT, such as increasing inventory turns and reducing scrap/waste, that motivate JIT adoption rather than other, arguably less central incentive aspects of JIT, such as product quality. Overall, our results are consistent with the predictions of the implicit “career” incentives Principal-Agent model but not with predictions of the standard explicit incentives Principal-Agent model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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