Bibliographic record
Abstract
The aim of this study is to explore the effect of training on organizational performance. Training techniques were categorized into two types: behavioural training techniques (on-the-job training) and cognitive training techniques (off-the-job training). Three behavioural techniques were selected – monitoring, coaching, and job rotation – and three cognitive techniques – role playing, lectures, and computer-based training. Training as an independent construct was measured based on these behavioural and cognitive techniques. On the other hand, organizational performance was measured based on subjective items related to the operational dimensions of organizations’ performance. A questionnaire-based survey was used to collect data from a sample consisting of 600 employees working at service organizations in Saudi Arabia. Of the questionnaires distributed to the sample, 478 were returned complete and valid for the analysis process. The Statistical Package of Social Sciences (SPSS) version 20 was used to analyse the collected data. The findings of the study confirmed that both behavioural techniques of training were significantly and positively related to organizational performance. In fact, the results identified job rotation as a main practice of on-the-job training techniques that lead to improved organizational performance. There is a statistically significant influence of other dimensions, such as coaching, monitoring, role playing, lectures, and computer-based training, on organizational performance. Despite the positive and direct impact of on-the-job training and off-the-job training on the dependent variable, organizational performance, on-the-job training has a larger impact on this construct. The results are presented and discussed, and recommendations, limitations, and future research directions are provided.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".