Tie Dissolution in Market Networks: A Theory of Vicarious Performance Feedback
Bibliographic record
Abstract
Managers need to periodically evaluate any exchange partner to decide whether to continue or dissolve the exchange tie, but doing so can be challenging because of causal ambiguity: it can be difficult to attribute organizational performance to any specific underlying factor. One way managers may evaluate their exchange partners is by observing the performance trajectories of competitors who rely on the same exchange partners. We propose a theory of vicarious performance feedback and test it in the context of Formula One motor racing. We find that a firm building a Formula One racing car is more likely to end an exchange relationship with an engine supplier after that supplier’s other customers experience an episode of poor performance relative to their historic track record. In line with an attention-based view of the firm, this behavior occurs when the firm’s own performance is below its aspiration level. This work extends our understanding of how managers use vicarious learning to supplement their direct experience when evaluating their exchange partners, expands our thinking about network dynamics by showing how network neighbors’ experiences can influence tie decisions made within a dyad, and contributes to the cognitive foundations of problemistic search by showing how external information is integrated into managers’ responses to their own firm’s underperformance.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".