Executive’s Perception on the Outcomes of In-House and Outdoor Training Programmes
Bibliographic record
Abstract
The purpose of the study is to understand the Executives’ perception on the outcomes of in-house and outdoor training programmes. Design/methodology/approach-Data from executives are collected online. The questionnaire was uploaded in the Google docs. Out of 180 executives, 74 executives responded to both questionnaires, which comprised the sample of our study. The collected data was analyzed by mean, standard deviation and MANOVA to know the training outcomes difference between in-house and outdoor training programs. Research Limitations-The executives of the company are based at different Locations, hence it is difficult to go and collect the data in person. Therefore the questionnaire was uploaded in Google docs for response. Practical Implications-It is important to understand the demands of a job and the background of the learners in order to provide training, which meets specific needs. At present both in-house and outdoor trainings are being conducted to executives to persuade their deeds and mind-set in the direction of the Organization. The success of the training program depends on the training outcomes. By studying these factors the company can evaluate the future need of training and development plan and also in selecting the right training programs for executives. Originality and Value-Even though outdoor training is being offered by many companies from last decade research was done in negligible way. This is the first attempt to compare the in-house and outdoor training programs. This type of evaluation is first time in the Indian context as well as in confectionary company.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".