Incentivizing supplier participation in buyer innovation: Experimental evidence of non‐optimal contractual behaviors
Bibliographic record
Abstract
Abstract Original equipment manufacturers increasingly involve suppliers in new product development (NPD) projects. How companies design a contract to motivate supplier participation is an important but under‐examined empirical question. Analytical studies have started to examine the optimal contract that aligns buyer‐supplier incentives in joint NPD projects, but empirical evidence is scarce about the actual contracts offered by buying companies. Bridging the analytical and empirical literature, this paper compares optimal contracting derived from a parsimonious analytical model with actual behaviors observed in an experiment. In particular, we focus on how project uncertainty, buying company effort share, and buyer risk aversion influence three contractual decisions: total investment level, revenue share and fixed fee. Our results indicate significant differences between the optimal and actual behaviors. We identify various types of non‐optimal contractual behaviors, which we explain from a risk aversion as well as a bounded rationality perspective. Overall, our findings contribute to the literature by showing that (1) the actual contractual behaviors could differ significantly from the optimal ones, (2) the actual contract design is sensitive to changes in project uncertainty and buying company effort share, and (3) the significant roles of risk aversion and bounded rationality in explaining the non‐optimal contractual behaviors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".