Linking job autonomy to employee engagement and exhaustion: the role of employee cognitive appraisal
Bibliographic record
Abstract
Abstract This research focuses on the impact of different forms of autonomy on employee engagement and exhaustion, as mediated by employee feelings of personal responsibility and cognitive appraisals of personal gain and loss. By integrating conservation of resources theory with a cognitive appraisal approach, we argue that decision-making autonomy, method autonomy, and scheduling autonomy represent qualitatively distinct autonomy resources that may differ in their associated potential for personal gain and loss. We propose personal gain appraisal as a psychological mechanism that leads to engagement (motivating pathway) and personal loss appraisal as a psychological mechanism that leads to exhaustion (strain-reducing pathway). Using data from 255 employees that responded to surveys at three time points, our findings suggest that the motivating potential of job autonomy on employee engagement refers to decision-making autonomy (via felt responsibility and personal gain appraisal) and method autonomy (via personal gain appraisal), but not to scheduling autonomy. In addition, the strain-reducing potential of job autonomy on employee exhaustion refers to decision-making autonomy (via felt responsibility and less personal loss appraisal) and scheduling autonomy (via less personal loss appraisal), but not to method autonomy. Our results yield several theoretical, empirical, and practical implications for the study of job autonomy.
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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 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".