Effects of Individual and Work Environment Characteristics on Training Effectiveness: Evidence from Skill Certification System for Automotive Industry in Thailand
Bibliographic record
Abstract
Previous research over the past two decades has argued Kirkpatrick’s model ignored the work environment and individual factors influencing training effectiveness. A focus of this study is to investigate four levels of Kirkpatrick’s model with a focus on moderating the influences of individual and work environment characteristic variables, which are learning motivation, self-efficacy, motivation to transfer, and social support. In the present study, we used path analysis to test the hypotheses. The results of this study expand our understanding of the progressive causal relationship of reaction, learning, and behavior to results. In particular, this study confirms the influence of the individual and work environment characteristic on training outcomes and it has implications for enhancing training effectiveness. Although the result of motivation to transfer as a moderating variable has negative effects on the relationship between learning and behavior, social support directly affects behavior change after training and moderates the relationship between learning and behavior. Furthermore, future research on training evaluation should consider the training design variables beyond the training course that may have interfered with the training outcomes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".