Revenue sharing bids of a loss-averse supplier for a new product development contract: a multi-method investigation
Bibliographic record
Abstract
Purpose When developing a new product, a buying firm solicits revenue sharing bids from two competing suppliers. Bidding behaviors of suppliers do not always align with predictions from rational agent models due to task uncertainty and bounded rationality, which could result in non-optimal supplier offers and ultimately hurt buying firm interests. This paper aims to discuss the aforementioned issues. Design/methodology/approach The authors built an analytical model that considers the impact of supplier technological risk, buyer–supplier coordination cost and supplier loss aversion on the optimal bid of the supplier. Next, using limited information processing capacity as a theoretic lens, the authors explore antecedents to the size of a focal supplier's bidding error, the absolute difference between the actual bid and the optimal bid. The authors used quantitative lab experimental data to test the hypotheses. Findings (1) Bounded rational bidders often fail to differentiate between relevant and irrelevant competitive information when placing bids, (2) loss aversion of a bidder significantly affects not only levels of bids, particularly for bidders with competitive disadvantages, but also sizes of the bidding error and (3) competitive information that has clearer performance implications are more influential in reducing sizes of bidding errors. Originality/value The results provide a comprehensive view of the bidding behaviors of a bounded rational supplier in an innovation outsourcing context with competition. With the results, managers now have a better understanding of behavioral influencers behind non-optimal supplier bids in an innovation outsourcing context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".