Collective Efficacy as a Mediator of the Relationship between Authentic Leadership and Well-being at Work
Bibliographic record
Abstract
Prior research, both theoretically and empirically, has revealed that authentic leadership is positively related to work attitudes and behaviors. But the fundamental mechanisms through which an authentic leader utilizes his/her influence on followers have not fully been explored. To provide a clear insight into authentic leadership and the inner working of the construct, further research is needed. This study addresses that needs and proposes that positive organizational outcomes are fundamentally related with the emotions felt by followers at work and followers' belief in their competences. Thus, the mechanisms that link the authentic leadership behavior to positive work outcomes are followers' well-being at work and collective efficacy perceptions. In this way, the aim of present study is to explore the influence of authentic leadership behavior on employees' well-being at work and also develop a clear understanding about the role of collective efficacy perceptions of employees' in that relationship. We conducted the research with full-time employed 556 construction engineers. Consequently, structural equation modeling results exposed that there is a positive relationship between authentic leadership and well-being at work. Moreover collective efficacy perception of employees' partially mediates this relationship. The implications of results and routes for future research are argued.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".