Peer Influence in the Workplace: The Moderating Role of Task Structures Within Organizations
Bibliographic record
Abstract
Peer influence is crucial in shaping work practices within organizations, yet the impact of formal organizational structures on this influence remains underexplored. We argue that task structures, which capture how tasks are allocated and configured within organizations, significantly affect peer interactions and influence. Specifically, we examine how two features of task structures—task variety and task similarity with peers—moderate peer influence in a highly consequential setting: physicians’ decisions to perform a birth via caesarean section (C-section) versus vaginal delivery. Using data on nearly 5 million births performed by more than 16,500 physicians across 915 hospitals in Brazil, we find that working alongside peers whose practice style (enduring preference) favors C-sections leads the focal physician to perform more C-sections, even after controlling for features of the mother and the pregnancy. This influence is significantly stronger for physicians with higher task variety and with higher task similarity with peers. Through post-hoc analyses, we provide evidence that the observed behaviors are consistent with a mechanism of information sharing between physicians. This study contributes to our understanding of peer influence in the workplace by showing how the task structure within organizations can either amplify or diminish peer influence. This awareness is particularly crucial for health care organizations in which such dynamics can have life-changing consequences.
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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 | 0.005 | 0.035 |
| 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".