Consequences of Job Insecurity and the Moderator Role of Occupational Group
Bibliographic record
Abstract
In recent decades, transformations in organizations and the labour market have produced an increase in employee job insecurity. In response to this situation, workers present different negative reactions. However, the intensity of these reactions varies across studies that have investigated the outcomes of job insecurity. One possible explanation for this inconsistency may lie in the influence of other factors, such as the occupational group (Sverke et al., 2002). The aim of this study is to provide additional evidence about the relationship between job insecurity and its outcomes (i.e., life satisfaction, job satisfaction, perceived performance and organizational commitment), and examine the moderator role of occupational group in this relationship. The sample was composed of 321 employees from different Spanish organizations. The results showed that job insecurity was directly and negatively related to life satisfaction, job satisfaction and organizational commitment, and they suggest that occupational group moderated relations between job insecurity and three studied outcomes. In the case of life satisfaction and perceived performance, this relationship was stronger among blue collar workers. The relationship between job insecurity and job satisfaction was stronger in white collar workers. The implications and limitations of this study are discussed.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".