Beyond <i>Moneyball</i> to social capital inside and out: The value of differentiated workforce experience ties to performance
Bibliographic record
Abstract
The differential impact of social capital among employees in strategic and support roles has received far less attention than that of human capital in talent management literature. Building on network closure theory and differentiated workforce theory, we examine the effect of strategic and support teams’ experience ties on team performance while controlling for human capital using current Moneyball‐inspired metrics for workforce quality. Using an 111‐year longitudinal data set of 15,837 Major League Baseball players from all 30 teams and 3,475,778 experience ties, we find that after accounting for the effect of team quality, managerial stability and reputation, and era effects, organizational experience ties and subsequent team performance have an inverted U‐shaped relationship for strategic roles and a U‐shaped relationship for support roles. Competitor experience ties have an inverted U‐shaped relationship on performance for strategic roles, yet the hypothesized U‐shaped relationship showed differences for different competency areas among support roles. This study highlights the value of social capital to team performance and the importance of differentiating human resource management (HRM) practices for strategic and support roles in 20 different competency areas. It also showcases how workforce analytics with big data can be applied to HRM and have value added impact on workforce and firm strategy execution.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".