The effect of alcohol consumption on workplace aggression: What's love (and job insecurity) got to do with it?
Bibliographic record
Abstract
Purpose The authors tested whether the effect of alcohol consumption during work hours on workplace aggression was influenced by the combined impact of individuals' job insecurity and love of the job. Design/methodology/approach The authors employed a time-lagged design whereby 325 working adults (166 men; 159 women) provided data at two time points. Respondents were asked to report their typical alcohol consumption volume in a workday, the extent to which they loved their job, and how insecure they felt about their job. Approximately one week later, respondents completed a workplace aggression measure. Findings A substantial positive relationship was observed between the volume of alcohol consumed during work hours and the likelihood of aggressive acts. Beyond this preliminary finding, the authors found evidence for a three-way interaction. It appears that the fear of losing a beloved job creates a condition under which the drinking-aggression relationship is particularly strong. Practical implications Besides formal rules deterring alcohol consumption during work hours, managers may look to implement measures that nurture a sense of job love and job security, which can be beneficial in preventing aggression resulting from drinking in the workplace. Originality/value By examining alcohol consumption during a typical workday, the study captures the contextual and proximal effects of drinking, which are often not observed in workplace-focused studies that operationalize alcohol consumption in general terms. The findings also suggest that if employees who drink during work hours are afraid of losing the job they love, a particularly stressful situation is created in which workplace aggression is more likely to happen.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".