Impact of Total Rewards on Animation Employees’ Engagement
Bibliographic record
Abstract
The researchers examine the impact of total rewards on engagement by multiple regression analysis in this paper. The sample for the study is 800 animation employees in South China. SPSS17.0 and AMOS18 are used in exploratory factor analysis and confirmatory factor analysis. The study proves that: (a) Total rewards are a multi-hierarchical and multi-dimensional construct which includes 7 first-order factors and 4 second-order factors. (b) Challenging working environment, appreciation and recognition, promotion opportunity and individual variable pay have significant positive impacts on employee engagement, and the contribution rate are 42.0%, 11.6%, 1.4% and 0.9% respectively. (c) Individual fixed salary, collective salary and spiritual rewards have no significant positive impacts on employee engagement. The study has further enriched the theories of total rewards and employee engagement and has provided the theoretical basis and empirical evidence supports to the management of the animation companies for them to carry out the incentive programs to the employees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".