The Impact of HR Analytics on the Training and Development Strategy - Private Sector Case Study in Lebanon
Bibliographic record
Abstract
This study investigated the impact of HR analytics on the training and development strategy of private organizations in Lebanon. It sought to test four hypotheses namely: There is a significant relationship between HR analytics use in large businesses and the development of employee skills; there is a significant relationship between HR analytics use in older businesses and the development of employee skills; there is a significant relationship between HR analytics use in large businesses and the retention of employees; and there is a significant relationship between HR analytics use in older businesses and the retention of employee. HR analytics has a significant influence on the development of employee skills and HR analytics has a significant influence on the development of HR training strategy. The study relied on a quantitative correlational research method with the help of an online questionnaire as the data collection instrument. A total of 302 respondents from the private sector in Lebanon returned valid responses to the questionnaire. The results validated each of the four hypotheses. They revealed that HR professionals rely on HR analytics to formulate employee development strategies. Data from HR analytics is used to predict potential outcomes of important HR and organization strategy decisions. In conclusion, the findings from this study imply that businesses should integrate HR professionals and HR analytics into the process of decision making and development strategy formulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".