The effect of green marketing, green supply chain and green human resources on business performance: Balanced scorecard approach
Bibliographic record
Abstract
Green management and its implementation and practices attracted the attention of both practitioners and academicians alike, which in part is due to its positive impact on the environment and sustainability, however, little is known about what really measures the effect of green management practices on business performance. Conventional measures of financial performance cannot reflect the proposed effect since green management is long-term oriented and not limited to financial performance only. This research explores the potentials of using balanced scorecard to measure the effect of green marketing, green supply chain and green human resources on the performance of the firms. Simple random sampling technique was followed in this research, and data were collected from 113 managers from different companies in service and manufacturing sectors in UAE. The findings indicate that green marketing was the major determinant of customers’ performance, green human resources was the major factor affecting learning and growth, and that green supply chain was the main influencer on financial performance and internal processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".