Revitalizing Colleague‐Specific Human Capital: Boomerang and Pipeline‐Based Hiring in a 41‐Year Multilevel Study of Employee Mobility
Bibliographic record
Abstract
ABSTRACT Amidst the decline of permanent employment contracts and the rapid shortening of career cycles, organizations often face challenges in fully capitalizing on employee mobility. This study adopts a multilevel perspective to explore how mobility impacts both individual and team performance, focusing on acquiring colleague‐specific human capital through two talent acquisition strategies: boomerang hiring and pipeline‐based hiring. Using a unique Major League Baseball database spanning 41 years, including 19,927 player‐year records and 1156 team‐year records, our analysis reveals that individuals engaged in boomerang and pipeline‐based hiring and possessing higher levels of individual colleague‐specific human capital, experience greater benefits from mobility in terms of individual performance. Moreover, these hiring strategies allow organizations to effectively harness colleague‐specific human capital. Specifically, team performance is positively influenced by a greater proportion of boomerang hiring through team colleague‐specific human capital resources. Similarly, a higher ratio of pipeline‐based hiring, alongside other recurrent hiring practices, positively impacts team performance through team colleague‐specific human capital resources. Our findings provide valuable insights for organizations aiming to rejuvenate their colleague‐specific human capital resources through strategic hiring practices to achieve sustained success.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".