Predicting temporary agency workers' behaviors
Bibliographic record
Abstract
Purpose This empirical study aims to determine whether justice perceptions formed in one context (i.e. the agency or the client) relate to work behaviors in another context (i.e. the client or the agency). To provide a balanced perspective, it seeks to examine both organizational citizenship behaviors (OCBs) and counterproductive workplace behaviors (CWBs). It also aims to understand how workers' “volition” or their attitudes towards temporary employment would affect their behaviors. Design/methodology/approach To test the hypotheses, 157 temporary agency workers were surveyed; these data were analyzed with structural equation modeling (SEM). To ensure that the measures were appropriate for the context of temporary agency employment, a two‐stage pretest was conducted. Findings The results suggest that temporary agency worker perceptions of interpersonal justice from their agencies and their client organizations “spillover” and are indeed related to their OCBs and CWBs in both contexts. Furthermore, the extent to which workers voluntarily chose temporary agency employment related to agency‐directed OCBs, while a preference for permanent employment related to client‐directed OCBs. Originality/value This study provides insight into the ways in which perceptions formed in one context (i.e. interpersonal justice) may spill over and affect behaviors in another context. The findings also contribute to the broader literature on how volition affects temporary agency worker behaviors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".