An Empirical Analysis of the Incentive-Action-Performance Chain of the Principal-Agent Model
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.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".