The Relationship Between Socioeconomic Status and Productivity of Female Garment Workers of Bangladesh: Mediating Role of Job Satisfaction and Job Engagement
Bibliographic record
Abstract
ABSTRACT The purpose of this study is to determine the relationship between productivity and socioeconomic characteristics, as well as the degree to which job satisfaction and engagement can strengthen or moderate this association. With a focus on the mediating roles of job satisfaction and job engagement, this study examines the complex relationship between the productivity of female textile workers and their socioeconomic level (SES). This study surveyed 375 Bangladeshi female garment workers using a structured questionnaire through face‐to‐face interactions and Google Forms. Convenience sampling was utilized for data collection, which took place in Bangladesh, a developing country, between October 21 and December 31, 2023. The analysis was performed using structural equation modeling (SEM) and SPSS. The findings show that job‐related factors positively influence work engagement (β = 0.116) and job satisfaction (β = 0.142). Job satisfaction significantly impacts job performance (β = 0.141), and both job performance (β = 0.128) and work engagement (β = 0.207) reduce the intention to quit. These results support all research hypotheses. Additionally, it reveals that organizational commitment also acts as a moderator and the substantial positive relationship, mediated by job satisfaction and engagement levels, between elevated socioeconomic status and enhanced productivity, which means higher socioeconomic status, along with job satisfaction and engagement, leads to increased productivity, especially for workers with better living conditions, education, and family support. This study stands out due to the serial mediation of work engagement and employee happiness in developing countries. Looking at hygiene issues, job happiness, and performance, this research helps us understand the complex dynamics that affect workplace outcomes under these conditions. The implications of these findings are profound for policymakers and industry leaders. Improving the socioeconomic status of female garment workers not only enhances their well‐being but also boosts productivity, which is crucial for the competitiveness of the Bangladeshi garment industry. It demonstrates the importance of comprehensive strategies for organizational well‐being.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".