A Decision Support Contract for Cost-Quality Trade-Off in Projects under Information Asymmetry
Bibliographic record
Abstract
The three key drivers of a project success include cost, completion time, and scope, the interplay of which have a significant impact on the decision making in project management. In this study, we propose a theoretical framework to be used as a Project Management Decision Support System for understanding and balancing the interplay between the project cost and quality, which is a key component of the project scope. To this end, we develop a Decision Support Contract (DSC) for a project manager when outsourcing to a contractor whose delivery outcome is subject to quality risk. On the one hand, to reduce the risk of project failure, the contractor can invest in a quality improvement effort, the cost of which is the contractor’s private information. On the other hand, the contractor’s decision on quality improvement is unobservable to the project manager. In designing the DSC, we consider both problems resulting in information asymmetry between the project manager and the contractor. We first obtain the first-best solution assuming that the cost efficiency of the contractor is publicly known, and then solve for the second-best optimal cost plus incentive fee (CPIF) contract under information asymmetry. Our comparative study between the first- and second-best contracts reveals that the project manager may prefer to incur efficiency loss due to underinvestment decision by the high-cost contractor to reduce the information rent demanded by the low-cost contractor. Finally, we compare the effectiveness of CPIF contract to that of fixed-price contract, which enables us to characterize the value of incentive fee term for the project manager. This latter analysis reveals that incentive-fee term is more valuable when the improvement effort is more likely to reduce the quality failure risk.
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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 | 0.013 | 0.034 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".