Uncertainty and Compensation Design in Strategic Interfirm Contracts
Bibliographic record
Abstract
ABSTRACT In strategic outsourcing contracts, a substantial portion of implementation occurs at the client's premises and requires integration of effort between the vendor and the client. Compensation design in such contracts involves trade‐offs between the higher (lower) incentive properties of fixed‐price (cost‐plus) contracts and their higher (lower) ex ante contracting and ex post adaptation costs. Uncertainty influences these trade‐offs and affects compensation design. We explore the compensation implications of two types of uncertainty—volatility and ambiguity—which are reflected in the client's accounting measures. Volatility reflects the unpredictability of changes in the future environment, which makes it difficult to contractually specify future contingencies. Ambiguity reflects lack of consensus about the nature, drivers, and value effects of uncertainty, which makes it difficult to contractually specify responses to contingencies if and when they occur. Volatility increases the likelihood of ex post adaptation costs, while ambiguity increases ex ante contracting costs; therefore, volatility and ambiguity decrease the attractiveness of fixed‐price contracts. We use accounting and market measures to calibrate volatility and ambiguity and examine their implications for compensation design and ex post renegotiation. Analysis of archival data for 455 strategic outsourcing contracts valued over $15 million indicates that volatility and ambiguity influence contract compensation design and renegotiation likelihood. These results hold even after controlling for asset specificity, task complexity, and relational factors. We conclude that accounting measures can provide signals of volatility and ambiguity and thereby influence compensation design in strategic interfirm contracts.
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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 | 0.005 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".