Going the Extra Mile (or Not): A Moderated Mediation Analysis of Job Resources, Abusive Leadership, Autonomous Motivation, and Extra-Role Performance
Bibliographic record
Abstract
Abusive leadership is particularly prevalent in nursing and it can have multiple adverse effects on performance at work. However, little research has examined whether and under what conditions abusive leadership may be detrimental to nurses’ extra-role performance. This cross-sectional study explores whether abusive leadership intensifies the effects of emotional job resources on autonomous motivation, a psychological mechanism that could be responsible for extra-role performance. Data were collected from dyads of registered French-Canadian nurses and their immediate supervisors (n = 99 dyads). The models were tested with path analysis using Mplus. Our results show that extra-role performance is positively associated with nurses’ job emotional resources and autonomous motivation, but negatively associated with abusive leadership. Nurses’ cynicism is also negatively associated with autonomous motivation. Importantly, the indirect relation between emotional resources and extra-role performance through autonomous motivation is moderated by abusive leadership, providing support for a moderated mediation effect. These results add to those supporting a similar moderated mediation mechanism to explain employee attitudes and demonstrate the relevance of self-determination theory in a work context. These findings reinforce the need to focus on the quality of leadership practices as well as interventions aimed at promoting the performance of nurses at work.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".