Effects of trust on price contract design in a supply chain
Bibliographic record
Abstract
This paper explores how trust impacts the buyer’s price contract design in a supply chain. Depending on the risk attitude toward each other, the buyer and the supplier build either goodwill trust or capability trust through their long-term relationship. By modelling the capability trust through a power function and the goodwill trust through an exponential function, the authors obtain the optimal price contract as a solution to a nonlinear ordinary differential equation and provide the explicit solution for the resulting four combinations. If the capability trust is built between the buyer and the supplier, then the optimal price contract will be concave, convex or linear w.r.t the supplier’s output, depending on both parities’ degrees of risk aversion. If goodwill trust exists between the buyer and the supplier, then the optimal price contract is always linear w.r.t the supplier’s output. If the buyer has capability trust toward the supplier but the supplier has goodwill trust toward the buyer, then the optimal price contract is always concave in the supplier’s output. In reverse order, then the optimal price contractis always convex in the supplier’s output. These interesting findings extend existing knowledge on the contingent conditions under which the buyer is more willing to provide a concave or convex contract to its supplier. The results also clarify the mechanism through which trust and risk aversion impact the price contract design in a supply chain.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".