Will We Ever Meet Again? The Relationship between Inter‐Firm Managerial Migration and the Circulation of Client Ties
Bibliographic record
Abstract
Abstract A large body of research shows that the migration of managers from one professional service firm to another weakens the old employer’s relationship with its clients, because migrating managers remove their relationship‐specific knowledge and expertise – i.e., human and social capital – from their old employers, redeploying it to their new employers. This study extends this research by introducing a bi‐directional perspective of social capital in which both firms and managers may exploit these relationship‐specific resources. We use theory on social capital to build arguments about how one form of manager mobility, manager migration between two service providers in a single market, can both lead and lag the movement of client ties between those providers, and signaling theory to hypothesize the conditions under which this is likely to occur. Analyses using longitudinal data on New York City advertising agencies generally support our arguments. Our findings contribute to theory and research on manager migration, social capital, and signaling, and raise new questions for how the portability of relationship‐specific social capital shapes markets.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".