Job engagement trajectories: Their associations with leader–member exchange and their implications for employees
Bibliographic record
Abstract
Abstract The present study seeks to achieve a dynamic understanding of employees' job engagement trajectories, and of their time‐structured associations with leader–member exchange (LMX) and outcomes related to psychological adaptation (turnover intentions, emotional exhaustion, job satisfaction and life satisfaction). A sample of 285 employees was surveyed three times (6 months apart) over a 1‐year period. Results revealed that employees' global job engagement followed high and stable trajectories, their specific cognitive and emotional job engagement followed slightly decreasing trajectories, and their specific physical engagement displayed non‐linear trajectories characterized by an initial decrease followed by a slight increase. Specific LMX contribution and LMX professional respect were associated with positive fluctuations in global job engagement, whereas global LMX was associated with positive fluctuations in specific emotional engagement. Specific LMX loyalty and LMX affect (at Time 1 only) were associated with positive fluctuations in specific physical engagement, whereas global LMX was negatively associated with these fluctuations. Higher global job engagement and specific emotional engagement were associated with negative fluctuations in turnover intentions and emotional exhaustion and with positive fluctuations in job satisfaction. Higher specific physical engagement was associated with negative fluctuations in job satisfaction, whereas higher specific cognitive engagement was associated with lower life satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".