Performance of green supply chain management: Investigating the role of reverse logistics and green procurement aspects in SMEs
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between the performance of Reverse Logistics and Green Supply Chain Management, to analyze the relationship between Green Procurement Aspects and the performance of Green Supply Chain Management. This research method is quantitative, the analysis of research data uses the partial least square structural equation model (SEM-PLS) with a statistical data processing tool, namely SmartPLS 4.0 software. Research data was obtained by distributing online questionnaires through social media designed using a Likert scale of 7. Respondents in this study were 670 SMEs owners in Java Island, Indonesia. The stages of data analysis are validity test, reliability test and significance test or hypothesis test. The results of this study indicate that Reverse Logistics has a positive and significant effect on the performance of Green Supply Chain Management, Green Procurement Aspects has a positive and significant effect on the performance of Green Supply Chain Management. The novelty of this study is the relationship model between Reverse Logistics variables, the performance of Green Supply Chain Management and Green Procurement in SMEs which was not found in previous studies. Culinary SMEs are expected to be able to participate in supporting environmentally sound development. This is because the concept of Green Supply Chain Management (GSCM) is a concept that aims to minimize the negative impact of an organization and its supply chain on the environment related to climate change, pollution and resources that are not too large. In order to support GSCM, it is necessary to evaluate the extent to which this concept is carried out by Culinary SMEs. By conducting this evaluation, it is hoped that the constraints and obstacles faced by SMEs in carrying out GSCM can be identified. For this reason, it is necessary to have support from related parties, in this case the government, to conduct socialization and counseling and assistance in implementing GSCM.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".