Understanding the "personological" basis of employee withdrawal: The influence of affective disposition on employee tardiness, early departure, and absenteeism.
Bibliographic record
Abstract
This study investigated the impact of positive affectivity (PA) and negative affectivity (NA) on employee tardiness, early departure, and absenteeism, controlling for demographic, job-related, and environmental variables. The 3 temporary withdrawal measures were collected from organizational records in the 12 months following the survey. The LISREL analysis was based on a sample of 362 blue-collar employees from a multinational automotive manufacturer. The results indicate that individuals high in PA were associated with increased tardiness and early departure but decreased absenteeism. Individuals high in NA were associated with increased early departure. In terms of moderator effects, job satisfaction had a significant negative impact for individuals low in PA in predicting tardiness and early departure, whereas job satisfaction displayed a significant negative relationship with early departure for individuals high in NA. Implications of the findings are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".