Getting a Bonus: Social Networks, Performance, and Reward Among Commercial Bankers
Bibliographic record
Abstract
Research on the effects of social networks on individual status attainment has exploded in recent years, but the results remain equivocal, varying across network structures, types of ties, and outcome variables. The focus in this literature has been on two primary outcomes: performance benefits and rewards (including promotion and compensation). These two types of outcomes have often been conflated, however, despite the fact that high levels of one do not guarantee high levels of the other. We examined the effects of job performance, network tie strength, and network structures on the size of the year-end bonuses received by 71 relationship officers in a major, multinational commercial bank. We found that in networks based on information acquisition, both strong ties and sparse networks are positively associated with high bonuses, as is the combination of the two. In networks based on approval and support for one's deals, neither tie strength nor density predicts bonus size, but the benefits of strong ties increase as network density increases. Our results demonstrate the importance of distinguishing networks based on collegial relations from those based on authority, as well as the importance of distinguishing the network factors that improve performance from those that generate favorable evaluations independent of performance.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".