Research Note—Information Technology, Contract Completeness, and Buyer-Supplier Relationships
Bibliographic record
Abstract
The theory of incomplete contracts has been used to study the relationship between buyers and suppliers following the deployment of modern information technology to facilitate coordination between them. Previous research has sought to explain anecdotal evidence from some industries on the recent reduction in the number of suppliers selected to do business with buyers by appealing to relationship-specific costs that suppliers may incur. In contrast, this paper emphasizes that information technology enables greater completeness of buyer-supplier contracts through more economical monitoring of additional dimensions of supplier performance. The number of terms included in the contract is an imperfect substitute for the number of suppliers. Based on this idea, alternative conditions are identified under which increased use of information technology leads to a reduction in the number of suppliers without invoking relationship-specific costs. We find that a substantial increase in contract completeness due to reduced cost of information technology could increase the cost per supplier even though the cost of coordination and the cost per term monitored decrease. Such an increase in the cost per supplier leads to a reduction in the number of suppliers with whom the buyer chooses to do business. Similarly, we find that if coordination cost is reduced when more information technology is deployed so that the number of suppliers in the buyer’s pool increases substantially, the buyer might choose to make the supplier contracts less complete, instead relying on a more market-oriented approach to finding a supplier with good fit.
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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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".