Procurement auctions and negotiations: An empirical comparison
Bibliographic record
Abstract
Real-world procurement transactions often involve multiple attributes and multiple vendors. Successful procurement involves vendor selection through appropriate market mechanisms. The advancement of information technologies has enabled different mechanisms to be applied to similar procurement situations. However, advantages and disadvantages of using such mechanisms remain unclear. The presented research compares two types of mechanisms: multi-attribute reverse auctions and multi-attribute multi-bilateral negotiations in e-procurement. Both laboratory and online experiments were carried out to examine their effects on the process, outcomes, and suppliers’ assessment. The results show that in procurement, reverse auctions were more efficient than negotiations in terms of the process. Auctions also led to greater gains for the buyers than negotiations, but the suppliers’ profit was lower in auctions. The buyer and the winning supplier jointly reached more efficient and balanced contracts in negotiations than in auctions. The results also show that the suppliers’ assessment was affected by their outcomes: the winning suppliers had a more positive assessment toward the process, outcomes, and the system. The findings are consistent in both the laboratory and the online settings. Finally, the implications of this study for practitioners and researchers are discussed.
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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 | 0.037 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".