Green Training and Development Revolutionizing Organizational Performance: The Moderating Role of Green Employee Involvement in the Bangladeshi Pharmaceutical Industry
Bibliographic record
Abstract
Green Human Resource Management (GHRM) has been found to positively affect job satisfaction (JS) and job performance as employees are more engaged and motivated to improve organizational performance (OP). However, few studies have investigated this phenomenon, and none have examined green employee involvement (GEI) within the Bangladeshi context. Therefore, this study examines the moderating role of GEI and the mediating role of JS and employee motivation (EM) on green training and development (GTD), organizational commitment (OC), and OP among pharmaceutical companies in Bangladesh and contributes to the theories of action and job performance and of ability-motivation-opportunity. Self-administered surveys were sent to executives working in 10 pharmaceutical companies in Bangladesh, and data were collected from 180 respondents using convenience sampling. These findings show that the GTD positively influenced OC and OP. Additionally, GEI played a significant moderating role between GTD and OC and GTD and OP. The study also revealed that JS played a significant mediating role between GTD and OC and, GTD, and OP. However, EM did not play a mediating role between GTD and OC and GTD and OP. Based on the research findings, pharmaceutical companies in Bangladesh should embrace GHRM practices, prioritize GTD, foster EI, and nurture JS, which are crucial for enhancing OC and OP. These recommendations contribute to a better understanding of sustainable HR practices and underscore the importance of aligning environmental goals with organizational success. The limitations of this study include limited generalizability, convenience sampling, cross-sectional design, self-report bias, limited mediation analysis, and omitted variables. To address these limitations, future research should employ a more diverse and representative sample, use longitudinal designs to establish causal relationships, incorporate qualitative methods to gather in-depth insights, and include a broader range of variables.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".