Building theory of green supply chain management for the chemical industry
Bibliographic record
Abstract
Purpose In Bangladesh, the chemical industry is one of the expanding industries based on current statistical data analysis. Green supply chain management (GSCM) is pivotal in order to compete with the global competition. This paper main aim is to discuss a systematic approach to build a structural outline. The purpose of the proposed structural outline is to predict the constructive implementation of GSCM especially on chemical industry in Bangladesh. Design/methodology/approach This proposed structural framework evaluates the suitable interrelationship next to the barriers of GSCM in the Bangladesh's chemical industry. Here, on the basis of literature review and survey from expert opinions by the use of the Delphi methodology in total eight barriers were concluded. Here additionally, MICMAC analyses were applied to determine the driving and dependence power. Furthermore, the frameworks outline for the barriers were included by means of total interpretive structural modeling (TISM) method. Findings Based on the analysis, the most significant barriers were found lack of supporting laws and guidance from the government and cost of disposal of hazardous products. Research limitations/implications The TISM technique only has implemented to develop the framework, whereas other tools or structural equation modeling (SEM) technique can be used to develop and validate the frameworks for barriers. Originality/value In this research, Delphi method questionnaire generated based on the GSCM in the Bangladesh chemical sector. This study will assist the industrial managers to assess and evaluate the crucial sectors, whereas they should give priority to apply the GSCM in the Bangladesh chemical industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".