Longitudinal trajectories of perceived organizational support: a growth mixture analysis
Bibliographic record
Abstract
Purpose This research aims to identify trajectories of employees' perceptions of organizational support (POS) over the course of an eight-month period and to document associations between these longitudinal trajectories and several outcomes related to employees' well-being (i.e. job satisfaction), attitudes (i.e. turnover intentions, affective commitment) and behaviors (i.e. voice behaviors). Design/methodology/approach POS ratings provided each four months by a sample of 747 employees were analyzed using person-centered growth mixture analyses. Findings Results revealed that longitudinal heterogeneity in POS trajectories was best captured by the identification of four distinct profiles of employees. Two of these profiles followed stable high (67.2%) and low (27.3%) POS trajectories, whereas the remaining profiles were characterized by increasing (2.2%) or decreasing (3.3%) POS trajectories. Our results showed that, by the end of the follow-up period, the most desirable outcome levels were associated, in order, with the increasing, high, low and decreasing trajectories. Practical implications This research has important implications by showing that perceptions of organizational support fluctuate over time for some employees and help better predicting valuable work-related outcomes. Originality/value These findings shed a new perspective on organizational support theory by adopting a dynamic perspective, and revealing that changes over time in POS are more potent predictors of valuable work-related outcomes than stable POS levels.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".