Information Sharing in a Supply Chain: Using Agency Theory to Guide the Design of Incentives
Bibliographic record
Abstract
Sharing accurate and timely supply and demand information throughout a supply chain can yield significant performance improvements to all members of the supply chain. Despite the benefits, many firms are reluctant to share information with their supply chain partners due to an unequal distribution of risks, costs, and benefits among the partners. The information shared will usually benefit the recipient, yet the majority of costs will be incurred by the provider. Many firms are also reluctant to share information due to the risk of it being divulged to competitors or used for opportunistic bargaining. This paper uses agency theory to (1) help explain the reasons firms are reluctant to share information and (2) guide the design of incentives to redistribute risk and encourage information sharing in a supply chain. A principal-agent model is described that suggests traditional fixed payment incentives or investments are insufficient for ensuring timely and accurate sharing of information. Instead, a mix of profit sharing, payments for sharing forecasts, and nonmonetary incentives is required. Using the model, managers can examine the feasibility of information sharing in their supply chain and devise appropriate strategies to manage and redistribute the risks, costs, and benefits among their supply chain partners. This paper also makes an important contribution to the literature by re-examining the role of agency theory in supply chain information sharing.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".