Unpacking the curvilinear relationship between negative affectivity, performance, and turnover intentions: The moderating effect of time-related work stress
Bibliographic record
Abstract
Abstract This study explores the relationships of negative affectivity with two frequently studied outcome variables job performance and turnover intentions. Conventional wisdom holds that negative affectivity has a harmful impact on both job performance and intentions to leave; however, we propose a more nuanced perspective using empirical and theoretical arguments (e.g., self-regulation theory) to highlight the functional effects of negative affectivity. To test our hypotheses, we collected self-reported and supervisor-reported data from seven organizations in Pakistan. The findings based on data collected from 280 employees show that while negative affectivity is detrimental for job performance, this effect is mitigated as negative affectivity increases. It further shows that the linear negative main effect of negative affectivity on job performance is more pronounced when employees experience less time-related work stress. Finally, the curvilinear relationship between negative affectivity and turnover intentions is moderated by time-related work stress. The relationship has a U shape at high levels of time-related work stress, whereas at low levels it has an inverted U shape. A discussion of the limitations, future research, and implications for theory building and practice conclude the article.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".