I Am So Tired… How Fatigue May Exacerbate Stress Reactions to Psychological Contract Breach
Bibliographic record
Abstract
Previous research showed that perceptions of psychological contract (PC) breach have undesirable individual and organizational consequences. Surprisingly, the PC literature has paid little to no attention to the relationship between PC breach perceptions and stress. A better understanding of how PC breach may elicit stress seems crucial, given that stress plays a key role in employees' physical and mental well-being. Based on Conservation of Resources Theory, we suggest that PC breach perceptions represent a perceived loss of valued resources, subsequently leading employees to experience higher stress levels resulting from emerging negative emotions. Moreover, we suggest that this mediated relationship is moderated by initial levels of fatigue, due to fatigue lowering the personal resources necessary to cope with breach events. To tests our hypotheses, we analyzed the multilevel data we obtained from two experience sampling designs (Study 1: 51 Belgian employees; Study 2: 53 US employees). Note that the unit of analysis is "observations" rather than "respondents," resulting in an effective sample size of 730 (Study 1) and 374 (Study 2) observations. In both studies, we found evidence for the mediating role of negative emotions in the PC breach-stress relationship. In the second study, we also found evidence for the moderating role of fatigue in the mediated PC breach-stress relationship. Implications for research and practice 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".