How Vicarious Learning Shapes Firms’ Relationship Networks with Third-Party Experts
Bibliographic record
Abstract
In some markets, firms can compete by forming relationships with experts outside the boundary of the firm—what we call third-party experts—who can serve to both legitimate the firm and influence demand for its products. Developing and maintaining relationships with these third-party experts is, however, a complicated endeavor and how they go about doing so is a core strategic decision for firms in these markets. Taking a network perspective (where we consider firms to have networks of relationships with third-party experts), and focusing on both tie formation and dissolution, we find that firms learn vicariously from their close competitors when determining whether to grow (or shrink) their relationship networks and which particular experts they should target. Furthermore, firms’ network-altering behaviors differ depending on whether a firms’ attention and resources are directed to relationships or are pulled toward an alternative strategic focus. Firms whose attention is focused elsewhere engage in less tie formation and tie dissolution but end up more reliant on what they learn vicariously from their competitors. This study contributes by highlighting how firms approach building relationships with third-party experts, by providing a dynamic perspective to firms’ strategic network building behaviors, and by generating further insight into the role that managerial attention and resource allocating play in shaping firm behavior.
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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 | 0.001 | 0.008 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".