A longitudinal analysis of the association between emotion regulation, job satisfaction, and intentions to quit
Bibliographic record
Abstract
Abstract The present longitudinal study explored the association between emotion regulation, defined as the conscious manipulation of one's public displays of emotion, and job satisfaction and intentions to quit. We predicted, based on an emotional dissonance model, that the suppression of unpleasant emotions decreases job satisfaction and increases intentions to quit. We propose a social interaction model that predicts that the amplification of pleasant emotions increases job satisfaction and decreases intentions to quit by improving the quality of interpersonal encounters at work. Data from 111 workers were gathered at two time points separated by four weeks. Advantages of the design included the use of longitudinal data and the statistical control for several personality, job, and demographic factors. Longitudinal regression analyses and tests of mediation revealed that, as predicted, (a) the suppression of unpleasant emotions decreases job satisfaction, which in turn increases intentions to quit, and (b) the amplification of pleasant emotions increases job satisfaction. Applied implications are discussed and suggestions for future research are offered. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".