Assessment of the Effect of Organizational Support and Investment in Employee Development on Affective Commitment
Bibliographic record
Abstract
The purpose of this research is to identify factors and conditions that improve the affective commitment of employees so as to increase the chances of organizations to retain their employees. This research took the form of a multiple regression analysis and assessed the ability of Perceived Investment in Employee Development and Perceived Organizational Support to predict Affective Commitment controlling nationality. The Perceived Investment in Employee Development questionnaire, Perceived Organizational Support assessment tool, and Affective Commitment survey have been utilized as instruments to collect data and a nonprobability purposive sampling technique involving 250 full-time employees in the service industry in Shanghai was carried out. It was observed that Perceived Investment in Employee Development and Perceived Organizational Support are significant predictors of Affective Commitment among various categories of employees that were distinguished in this study. It was found that employees welcome developmental programs where Perceived Organizational Support is high. It was suggested that endeavors to retain employees in China should not just implement observations made in Western countries but consider the socio-economic realties of the country so as to be effective. More light needs to be thrown on the predictors of Affective Commitment in China and this research recommended areas that require further investigations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".