Job insecurity and innovative work behaviour: A moderated mediation model of intrinsic motivation and trait mindfulness
Bibliographic record
Abstract
Research has disregarded the processes and boundary conditions associated with the effects of job insecurity on innovative work behaviour. Combining the job demands-resources and the self-determination perspectives, the present study develops and tests a first-stage moderated mediation model that identifies intrinsic motivation as a key mechanism accounting for a negative effect of job insecurity on innovative behaviour and trait mindfulness as a buffer against the detrimental impact of job insecurity on intrinsic motivation and, indirectly, innovative work behaviour. Two time-lagged studies-a two-wave study of 138 employees from Canadian firms and a three-wave study of 157 employees from US firms-were conducted to test the hypothesized model. Supporting our predictions, intrinsic motivation mediated a negative relationship between job insecurity and innovative work behaviour. Moreover, high levels of trait mindfulness were observed to attenuate the negative relationship of job insecurity with intrinsic motivation and, indirectly, innovative behaviour. These findings contribute to the literature by disclosing the processes linking job insecurity with impaired work outcomes and help to elucidate how and when employee can keep their innovative potential alive in spite of insecure work conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".