On the psychological and motivational processes linking job characteristics to employee functioning: Insights from self-determination theory
Bibliographic record
Abstract
The aim of this study was to provide insight into the differential relationships between job characteristics (job demands and resources) and employee functioning by examining the psychological and motivational processes involved. Drawing on self-determination theory, we tested a model in which job demands are positively related to negative manifestations of employee functioning (psychological distress and psychosomatic complaints) through psychological need frustration and low-quality work motivation (controlled motivation), whereas job resources are positively related to positive manifestations of employee functioning (work engagement and job performance) through need satisfaction and high-quality work motivation (autonomous motivation). Data were collected from 699 Canadian nurses. Structural equation modelling (SEM) results support the proposed model: psychological needs and work motivation partially mediated the relationship between job characteristics and employee functioning. Specifically, job demands negatively predicted employee functioning (high distress and psychosomatic complaints, low engagement and performance) through need frustration and controlled motivation. In contrast, while positively predicting need satisfaction and negatively predicting need frustration, job resources fostered optimal work motivation (more autonomous and less controlled motivation) and employee functioning. The implications for self-determination theory (SDT) and research on occupational health and stress are discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".