Role of Perceived Organizational Support in Linking Employee-Oriented SR-HRM and Affective Commitment Among RMG Workers in Bangladesh
Bibliographic record
Abstract
The purpose of this study is to determine how socially responsible human resource management affects the commitment of RMG employees in Bangladesh. Data was collected via a questionnaire survey from workers and employees of different RMG sectors in Dhaka and also through the online surveys using convenience sampling technique. With the use of an online survey, the questionnaire was created as a close-end survey with 5-point Likert scales. The questionnaire was divided into two sections, Section A displaying the respondents' demographic information, and Section B asking about the respondents' commitment to their jobs, compliance with the law, support for CSR initiatives, and HRM's employee orientation. There were 150 respondents, and the SPSS version 21.0 was used to analyze the frequency distribution, coefficient, linear regression, and multiple regressions to interpret the variables utilized in this study. The projected results state that employee commitment in the RMG sectors will affect legal compliances in HR, CSR initiatives, employee-focused HRM, and general HRM facilities. By concentrating on the influence of inclusive SR-HRM in relation to employee commitment to RMG in the context of Bangladesh, the study adds to the body of existing literature in the subject. According to the results, there is a strong correlation between socially conscious HRM and employee commitment in Bangladesh's RMG industry.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".