Buyer–Supplier Relationship Dynamics in Buyers’ Bankruptcy Survival
Bibliographic record
Abstract
A bankrupt buyer firm's interactions with its suppliers during bankruptcy have critical implications for both parties and for the broader economy, yet these interactions remain poorly understood. The authors build on research on buyer–supplier relationship dynamics to demonstrate that accommodative and exploitative velocities—the rate and direction of change in the corresponding acts—serve as signals affecting bankruptcy survival. They show how signal characteristics (i.e., the variability in accommodative and exploitative acts) and signaler characteristics (i.e., whether the party undertaking the acts is the buyer or its suppliers) moderate the impact of accommodative and exploitative velocities on bankruptcy survival. Study 1 examines the bankruptcy survival outcome of 310 U.S. bankruptcies over 14 years and finds that a 1% increase in accommodative (exploitative) velocity increases (decreases) the buyer's survival by 39% (33%). Further, variability in accommodative acts weakens their effect, and suppliers' (vs. the buyer's) accommodative and exploitative velocities are less deterministic of the buyer's bankruptcy survival. Study 2 uses a scenario-based experiment to shed light on the mechanism underlying the impact of the two velocities on bankruptcy survival. The findings from both studies demonstrate the key role played by buyer–supplier interactions in a buyer's bankruptcy survival.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".