Training Transfer: A New Model in the United Arab Emirates General Education Sector—Hybridization of the Theory of Planned Behavior with the Training Transfer Model
Bibliographic record
Abstract
This study investigates variables in training transfer in the general education (school) sector of the United Arab Emirates (UAE) by hybridizing the established training transfer model and the theory of planned behavior (TPB). The hybridized model employs four variables: (i) supervisor support, (ii) training design, (iii) intention to transfer, and (iv) training transfer. This model is used to test nine hypotheses. The study sample comprised 225 employees from the UAE general education sector. Study participants (respondents to a questionnaire) were recruited by simple random sampling. The study questionnaire data was analyzed using Partial least squares structural equation modeling PLS-SEM. The study model had a good fit confirming a good fit of the hypothesized model to the empirical data. Eight out of nine hypotheses were accepted. The study is generally parallel with TPB. It demonstrates that intention to transfer has a dominant and central (mediating) influence on transfer process and transfer behavior. Remarkably, supervisor support is important only in the pre-training phase. For the UAE education sector to succeed in effective training transfer, supervisors must be properly trained to design training programs, particularly to enhance the trainee’s intention to apply training on the job. This study proved empirically that designing training is a critical influence of a trainee’s intention to apply training. Training design and intention to transfer are mediators and play a central role in promoting the training transfer process. Future studies should focus on including TPB and intention in the training transfer researches.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".