Paternalistic leadership and employee well-being: a moderated mediation model
Bibliographic record
Abstract
Purpose This paper aims to explore the psychological mechanism in the relationship between paternalistic leadership (PL) and employee well-being (EWB) in cross-cultural nonprofit organizations. It also aims to further promote the integration of research on PL and self-concept by examining the relationship between PL and collective self-concept (CSC). Design/methodology/approach Data were collected on 72 supervisors and 233 expatriate Chinese teachers from 42 Confucius Institutes and 15 Confucius classrooms in Canada and the USA. Findings PL has a significant effect on EWB. Benevolent and moral leadership are positively related to CSC, while authoritarian leadership is negatively related to CSC. CSC mediates the relationship between PL and EWB. Furthermore, employees’ cross-cultural adaptability positively moderates the relationship between CSC and EWB; the indirect effect between PL and EWB via CSC is stronger for employees with stronger cross-cultural adaptability. Originality/value This is the first study that has examined the psychological mechanism under which PL affects EWB in cross-cultural nonprofit organizations. It contributes to the integration of research on PL and CSC by examining its relationship for the first time. It provides important implications for improving the well-being of expatriate employees in cross-cultural organizations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".