The Role of Design Factors in Influencing Training Transfer among Small Businesswomen
Bibliographic record
Abstract
The objective of this research is to investigate the effect of design factors which consist of training content, training delivery, trainer competency and opportunity to use on small businesswomen’s goal setting activities. The instrument for this research is adapted and modified from the Training Transfer Model and Model for Excellence (American Society of Training and Development Competency Research). Four independent variables: training content, training delivery and trainer’s competency and opportunity to use; and goal setting as dependent variable formed the framework for this research. Multiple regressions were used to investigate the relationship between design factors and goal setting. Findings from a survey of 246 small businesswomen attending training programs organized by government agencies showed that opportunity to use made the strongest contribution towards goal setting followed by training content, trainer’s competency and training delivery. Awareness on the constraints or barriers in the design factors can assist the primary stakeholders (organizer and trainers) and human resource personnel in developing effective training programs. Thus, this alertness can help to create a fair situation for them to accomplish their training objectives. Finally it is also beneficial to the trainees to transfer the knowledge and skills to their own businesses.
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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 | 0.005 | 0.028 |
| 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".