A Study on Employee Voice and its Effect on Work Engagement: Explicating from the Turkish Teachers’ Perspectives
Bibliographic record
Abstract
The objective of the present study is to investigate the effect of employee voice on work engagement according to the views of the Turkish teachers. The sample of the study consisted of 713 teachers randomly selected at 40 public schools from kindergarten to high school from Ayaş, Beypazarı, Güdül and Sincan districts of Ankara, the capital city of Turkey. In order to collect the data for this study, Employee Voice and Work Engagement scales were used. All scales were adapted to the school environment by translation and back translation method. The construct validity and reliability of the scales were examined through Exploratory Factor Analysis, Confirmatory Factor Analysis and Cronbach Alfa, Composite Reliability, Average Variance Explained values. In the first step of the data analysis process; arithmetic means, standard deviations, skewness and kurtosis values were calculated. Relations between the variables were measured by the Pearson product-moment correlation coefficient and regression analysis. Consequently, the results of study show that there is a positive and significant relationship between employee voice and work engagement. Besides, employee voice is a significant predictor for work engagement. The findings of the study exhibit that the level of teachers’ employee voice and work engagement are high degree.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".