"Multiple Supports, Commitment, Citizenship Behaviors, and Passive Leadership at the Hospital"
Bibliographic record
Abstract
Purpose: This paper examines the moderating role of passive leadership in the relationships of perceived support from organization, coworkers, and physicians to affective commitment and organizational citizenship behavior (OCB) among hospital employees. Design/methodology/approach: Using a sample of 182 hospital employees and a time-lagged design in which predictors and moderator were assessed at Time 1 and self-reported OCB was obtained 2.5 years later, we examined whether passive leadership moderated the relationships of perceived supports to commitment and their indirect effects on OCB. Findings: Analyses reveal that passive leadership is associated with a weaker relationship between perceived supports and commitment and that perceived support from coworkers and physicians were indirectly related to OCB only when supervisors displayed low passive leadership. Research limitations/implications: Although data were self-reported, our analyses show that method variance account for only 6% of the variance among constructs. Findings contribute to highlight the boundary conditions associated with perceived support and establish that passive leadership severely limits the beneficial effects expected from support available to employees. Practical implications: Findings suggest that supervisors should be trained not only on improving positive leadership skills but also on reducing passive behaviors in the face of problems in their teams. Originality/value: This study extends our understanding of social exchange processes in organizations and invites managers and researchers to look at factors that slow down the development of social exchange relationships with employees.
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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 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".