Investigating towards the sustainable green marketing environment of readymade apparel industries: A structural equation modelling approach
Bibliographic record
Abstract
Green marketing (GM) has frequently been seen as a prevalent phenomenon influencing companies' operations and functions. This article analyses GM techniques in India's ready-made apparel (RMA) industry by presenting a trio of viewpoints on the subject. It offers a systematic framework for the factors impacting the use of green marketing techniques. GM practices are evaluated regarding the impact on the environment, society, and the economy. Partial least square-structural equation modelling (PLS-SEM) is used to conduct empirical tests of the framework, focusing on data collected from a survey that evaluates eco-friendly marketing practices. The results show that environmental, social, and economic factors are beneficially interconnected. When assessing GM practice, the PLS-SEM estimation shows that the relationship between economic and environmental sustainability has a significant proportion of values. However, more data on RMA industries' environmental and social effects must be collected. Additionally, according to the results of the PLS-SEM model, there are considerable differences between actual and expected GM adoption developments and perspectives among various industrial firms, particularly for managing waste and pollution of water. Given the nation's rapid socio-economic growth and technical improvement, social-level performance has minimal impact on GM strategy for RMA industries. Moreover, the research makes some recommendations emphasizing the discovered model's component in motivating commercial organizations to get involved in socio-economic activities that promote the environment, changing the focus of prospective GM areas of study.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".