Organisational commitment through training and development : to what extent does training and development increase organisational commitment?
Bibliographic record
Abstract
Organisations around the globe are struggling with attracting talented employees and retaining them. Nowadays people do not need to stay at the same company for their entire career and are more likely to switch after every couple of years. The COVID pandemic did not help making working online at home a regular thing and work/life balance a big point on every agenda. \n \nRetraining new staff is costly for companies and therefore it is important to create organisational commitment with employees. By evaluating the value of retention factors amongst employees, organisations have an overview of where to invest in. \n \nThis study investigated the influence of training and development as a retention factor on the organisational commitment of store managers at a Canadian retail company. This study aimed to understand the value of training and development, what type of organisational commitment are found at the company, and how the company could further develop their training and development programme. \n \nResearch findings indicated that training and development was not valued as important, and that not all organisational commitments could be linked to the programme. The main reason was workload, lack of consistency or general interest in the programme.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".