Transformational leadership, work-family conflict and enrichment, and commitment
Bibliographic record
Abstract
Bien que les liens entre le leadership transformationnel et l’attachement organisationnel soient bien documentés, peu de recherches ont été menées sur les mécanismes explicatifs de ces relations. Dans cette étude, nous proposons que les relations entre le leadership transformationnel et l’attachement organisationnel soient médiées par le conflit et l’enrichissement travail-famille. Un questionnaire a été distribué à 600 salariés (225 hommes et 375 femmes) de plusieurs entreprises françaises. Des analyses en équations structurelles ont montré que les relations entre le leadership transformationnel et quatre composantes de l’attachement organisationnel (i.e., attachement affectif, attachement normatif, attachement par sacrifice perçu et attachement par manque d’alternatives) sont partiellement médiées par le conflit et l’enrichissement travail-famille. En adoptant des comportements de leadership transformationnel, les managers peuvent améliorer les interactions entre les sphères professionnelle et privée et indirectement faciliter le développement de formes positives d’attachement organisationnel (attachement affectif, attachement normatif, attachement par sacrifice perçu), tout en réduisant le conflit travail-famille et l’attachement par manque d’alternatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".