Requirement or Promise? An Analysis of the First‐Mover Advantage in Quality Contracting
Bibliographic record
Abstract
Quality contracting is critical and challenging due to the many unique issues related to quality. In this study, we analyze the first‐mover right in quality contracting by considering two different strategies for the buyer: the quality requirement strategy (QR) where buyer moves first by posting quality requirement to suppliers and quality promise strategy (QP) where buyer voluntarily gives up the first‐mover right to suppliers to ask them to promise quality. We study which strategy (1) better encourages suppliers' quality improvement efforts and (2) leads to a higher expected profit for the buyer. To analyze the drivers behind the buyer's choice between QR and QP, we start with the basic model where buyer faces only one supplier who has the opportunity to make quality improvements. We then gradually add other business features such as information asymmetry and supplier competition, analyzing how each feature adds/changes the driving forces and how they interact in the buyer's decision between QR and QP. We consider both the case where the wholesale price is fixed (when the buyer has the power to dictate price or price is set by the market) and the case where the wholesale price is included as a variable (when price is part of the negotiation). We find that QP always leads to the first‐best quality efforts from the supplier(s) while QR limits their efforts. However, this does not guarantee higher expected profit for the buyer under QP. We provide insightful guidelines in buyer's choice between QP and QR. This research enriches the limited literature on quality contracting with quality improvement opportunity and asymmetric information.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".