Formal supervisors' role in stimulating team members' informal leader emergence: Supervisor and member status as critical moderators
Bibliographic record
Abstract
Summary Teams can benefit markedly when formal supervisors stimulate their individual members' emergence as informal leaders. Combining insights from informal leadership research and social learning theory, we cast supervisors' role modeling of initiating structure and consideration behaviors as seemingly straightforward means of achieving this—but we suggest that the success of such role modeling critically hinges on supervisors' as well as members' status in the team. Results from a study of 220 nurses across 48 teams showed, accordingly, that a supervisor's initiating structure promoted individual members' informal leader emergence by increasing members' respective behavior. This indirect relationship only materialized, however, among relatively high‐status supervisors and relatively low‐status members. Moreover, although supervisors' and members' consideration were positively related (among relatively high‐status supervisors and largely irrespective of a member's status), such behavior did not influence members' emergence as informal leaders. Together, these findings offer novel insights into how, when, and why formal supervisors may aid their team members' attainment of informal leader roles. They shed new light on the complexity of formal–informal leadership linkages, with both supervisors' and members' standing in the team representing crucial, yet heretofore largely unexamined boundary conditions for formal supervisors' respective influence.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".