Antecedents of Psychological Contract Breach: The Role of Job Demands, Job Resources, and Affect
Bibliographic record
Abstract
While it has been shown that psychological contract breach leads to detrimental outcomes, relatively little is known about factors leading to perceptions of breach. We examine if job demands and resources predict breach perceptions. We argue that perceiving high demands elicits negative affect, while perceiving high resources stimulates positive affect. Positive and negative affect, in turn, influence the likelihood that psychological contract breaches are perceived. We conducted two experience sampling studies to test our hypotheses: the first using daily surveys in a sample of volunteers, the second using weekly surveys in samples of volunteers and paid employees. Our results confirm that job demands and resources are associated with negative and positive affect respectively. Mediation analyses revealed that people who experienced high job resources were less likely to report psychological contract breach, because they experienced high levels of positive affect. The mediating role of negative affect was more complex, as it increased the likelihood to perceive psychological contract breach, but only in the short-term.
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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 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".