Examining the frustration-aggression model among Tunisian blue-collar workers
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the links between work stressors, perceived stress, emotional exhaustion, and workplace aggression, using the traits of negative affectivity and external locus of control as individual moderators. Design/methodology/approach – Data were collected using a survey questionnaire among 477 blue-collar workers from a Tunisian manufacturing company. Findings – Results indicate that perceived stress mediates a positive relationship between work stressors (quantitative workload, role ambiguity, and interpersonal conflicts) and emotional exhaustion. Moreover, the relationship between quantitative workload and interpersonal conflicts and perceived stress is stronger among individuals with high levels of negative affectivity. Similarly, the relationship between quantitative workload and perceived stress is stronger at high levels of external locus of control. Finally, emotional exhaustion mediates a positive relationship between perceived stress and interpersonal and organizational aggression. Practical implications – The findings suggest that Tunisian organizations may reduce perceived stress and aggressive behavior among blue-collar workers through reducing quantitative workload, role ambiguity, and interpersonal conflicts. Moreover, specific training programs, job redesign, and formal mentorship that provide employees with improved social skills can also be recommended as soon as early signs of frustration or intentions to misbehave appear. Finally, leadership development practices may help supervisors better manage workplace stressors and reduce the occurrence of workplace aggression. Originality/value – The current study is an initial attempt to look at an integrated model of stress and aggression among blue-collar workers in Tunisia. While some of the findings are consistent with the literature, others might reflect the unique aspects of the Tunisian culture.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".