Electronic reverse auction configuration and its impact on buyer price and supplier perceptions of opportunism: A laboratory experiment
Bibliographic record
Abstract
Abstract Buying organizations are increasingly using electronic reverse auctions (eRAs) to source from suppliers. However, recent quasi‐experimental and field research has suggested that the use of this sourcing technique can create perceptions of opportunism among participating suppliers. Yet from the buyer's perspective, online reverse auctions can yield lower purchase prices. Given the many ways in which to configure on‐line auctions, we extend existing research by using a laboratory experiment to investigate how different reverse auction configurations jointly influence bid price and suppliers’ perceptions of buyer opportunism. Our findings suggest that supplier bid prices decrease over time as they participate in more eRAs, regardless of the configuration of auction parameters. However, the combination of rank (versus price) visibility, high (versus low) supplier need to win a contract, and six (versus three) competitors was significantly more effective than other combinations of variables in immediately reducing bid prices. The data also indicated that when suppliers’ bids dropped substantially across auctions, their perceptions of opportunism increased. Notably, auction parameter combinations such as price visibility, three competitors, and low need for the contract yielded comparably low bids by the third auction, without any increases in perceived buyer opportunism.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".